Sample processing method and system and electronic equipment

Abstract

The invention discloses a sample processing method and system and electronic equipment, and relates to the technical field of large model technology and artificial intelligence. The method comprises the following steps: acquiring a current sample set for training a to-be-processed model; analyzing the current sample set by using a large model to obtain prompt information which is used for representing rules satisfied by the samples marked as the abnormal state in the current sample set; the large model is controlled to generate an abnormal monitoring sample matched with the prompt information, and the abnormal monitoring sample meets the rule; the abnormal monitoring samples are marked, enhanced samples are generated, and the enhanced samples at least comprise enhanced samples matched with the abnormal monitoring samples. The technical problem that effective samples are difficult to generate is solved.

Description

Technical Field

[0001] The present application relates to the fields of large model technology and artificial intelligence technology, and specifically, to a sample processing method, system and electronic device. Background Art

[0002] In low-resource scenarios, small models are usually limited by the amount of training data and cannot perform well. Although large models have strong task generalization capabilities, the cost of reasoning and deployment is high and the usage scenarios are limited. In this case, large models can be used to generate targeted training data, that is, the large model is used to enhance the training sample set, and the enhanced data is used to train the small model. However, not all data enhanced in this way is useful. There may be some redundant data, which makes the training of the small model repeated. There may also be some lack of content that is useful for the training of the small model, which makes the small model unable to learn relevant content. There is still a technical problem of difficulty in generating effective samples.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a sample processing method, system and electronic device to at least solve the technical problem that it is difficult to generate valid samples.

[0005] According to one aspect of an embodiment of the present application, a method for processing a sample is provided. The method may include: obtaining a current sample set for training a model to be processed; using a large model to analyze the current sample set to obtain prompt information, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; controlling the large model to generate an abnormal monitoring sample that matches the prompt information, wherein the abnormal monitoring sample satisfies the rule; marking the abnormal monitoring sample to generate an enhanced sample, wherein the enhanced sample at least includes an enhanced sample that matches the abnormal monitoring sample.

[0006] According to another aspect of the embodiment of the present application, a method for processing a sample is also provided. The method may include: responding to an inquiry message received in a dialogue interface, wherein the inquiry message is used to request enhanced processing of the current sample set, and the current sample set is used to train a model to be processed; in the dialogue interface, displaying the current sample set; using a large model to analyze the current sample set, and displaying the generated prompt information on the dialogue interface, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; displaying at least an abnormal monitoring sample matching the prompt information on the dialogue interface, wherein the abnormal monitoring sample is generated based on the large model; displaying at least an enhanced sample generated by marking the abnormal monitoring sample on the dialogue interface, wherein the enhanced sample at least includes an enhanced sample matching the abnormal monitoring sample; based on the enhanced sample, generating a reply message corresponding to the inquiry message and displaying it on the dialogue interface.

[0007] According to another aspect of the embodiment of the present application, a method for processing a sample is also provided. The method may include: determining a model to be processed; using a large model to analyze the current sample set of the model to be processed, and generating prompt information, wherein the current sample set is used to train the model to be processed, and the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; at least controlling the large model to generate abnormal monitoring samples that match the prompt information, wherein the abnormal monitoring samples satisfy the rules; at least marking the abnormal monitoring samples to generate enhanced samples, wherein the enhanced samples at least include enhanced samples that match the abnormal monitoring samples; using the enhanced samples as samples of the sample set, training the trained model to be processed, and obtaining the target model.

[0008] According to another aspect of the embodiment of the present application, a sample processing device is also provided. The device may include: an acquisition unit, used to acquire a current sample set for training a model to be processed; an analysis unit, used to analyze the current sample set using a large model to obtain prompt information, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; a first control unit, used to control the large model to generate an abnormal monitoring sample matching the prompt information, wherein the abnormal monitoring sample satisfies the rule; a first marking unit, used to mark the abnormal monitoring sample and generate an enhanced sample, wherein the enhanced sample at least includes an enhanced sample matching the abnormal monitoring sample.

[0009] According to another aspect of the embodiment of the present application, a sample processing device is also provided. The device may include: a receiving unit, which is used to respond to the inquiry information received in the dialogue interface, wherein the inquiry information is used to request the current sample set to be enhanced, and the current sample set is used to train the model to be processed; a first display unit, which is used to display the current sample set in the dialogue interface; a second display unit, which is used to analyze the current sample set using the large model, and display the generated prompt information on the dialogue interface, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; a third display unit, which is used to display at least the abnormal monitoring samples matching the prompt information on the dialogue interface, wherein the abnormal monitoring samples are generated based on the large model; a fourth display unit, which is used to display at least the enhanced samples generated by marking the abnormal monitoring samples on the dialogue interface, wherein the enhanced samples at least include the enhanced samples matching the abnormal monitoring samples; a first generating unit, which is used to generate the reply information corresponding to the inquiry information based on the enhanced samples and display it on the dialogue interface.

[0010] According to another aspect of the embodiment of the present application, a sample processing device is also provided. The device may include: a determination unit for determining a model to be processed; a generation unit for using a large model to analyze the current sample set of the model to be processed and generate prompt information, wherein the current sample set is used to train the model to be processed, and the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; a second control unit for at least controlling the large model to generate abnormal monitoring samples matching the prompt information, wherein the abnormal monitoring samples satisfy the rules; a second marking unit for at least marking the abnormal monitoring samples and generating enhanced samples, wherein the enhanced samples at least include enhanced samples matching the abnormal monitoring samples; a training unit for using the enhanced samples as samples of the sample set to train the trained model to be processed and obtain a target model.

[0011] According to another aspect of the embodiment of the present application, a sample processing system is also provided. The system may include: a data identification end, used to identify the current sample set of the model to be processed, wherein the current sample set is used to train the model to be processed; a data enhancement end, used to use a large model to analyze the current sample set to obtain prompt information, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; at least control the large model to generate abnormal monitoring samples that match the prompt information, wherein the abnormal monitoring samples satisfy the rules; at least mark the abnormal monitoring samples to generate enhanced samples, wherein the enhanced samples at least include enhanced samples that match the abnormal monitoring samples; a model training end, used to at least use the enhanced samples as samples of the sample set to train the trained model to be processed to obtain a target model.

[0012] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory and a processor, the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, and the computer-executable instructions are executed by the processor to perform steps of a sample processing method.

[0013] According to another aspect of an embodiment of the present application, a computer-readable storage medium is further provided, the computer-readable storage medium comprising a stored program, wherein when the program processor runs, the device where the computer storage medium is located is controlled to execute the steps of the sample processing method.

[0014] In an embodiment of the present application, the current sample set used to train the model to be processed can be obtained first. Since the number of samples contained in the current sample set is small, based on this, the large model can be used to analyze the samples in the current sample set to obtain prompt information, which is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set, that is, the rules satisfied by the samples predicted incorrectly by the model to be processed. Afterwards, the large model can be controlled to generate abnormal monitoring samples that match the prompt information, and the abnormal monitoring samples can be marked to generate enhanced samples, which at least include enhanced samples that match the abnormal monitoring samples. Since the abnormal monitoring samples meet the rules satisfied by the samples predicted incorrectly by the model to be processed, based on this, when the enhanced samples are used to train the model to be processed, the model to be processed can be trained in a targeted manner, so that the model to be processed learns the samples predicted incorrectly before, and avoids training on useless samples, achieving the technical effect of using a large model to generate effective training samples in a small sample scenario, thereby improving the performance of the initial model in a small sample scenario, thereby solving the technical problem of difficulty in generating effective samples.

[0015] It is easy to notice that the above general description and the following detailed description are only for the purpose of exemplifying and explaining the present application, and do not constitute a limitation of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 is a schematic diagram of an application scenario of a sample processing method according to an embodiment of the present application;

[0018] Figure 2 is a flow chart of a sample processing method according to an embodiment of the present application;

[0019] Figure 3is a flow chart of another sample processing method according to an embodiment of the present application;

[0020] Figure 4 is a flow chart of another sample processing method according to an embodiment of the present application;

[0021] Figure 5 is a schematic diagram of a sample processing system according to an embodiment of the present application;

[0022] Figure 6 is a schematic diagram of a sample processing and model training method according to an embodiment of the present application;

[0023] Figure 7 is a schematic diagram of a sample processing device according to an embodiment of the present application;

[0024] Figure 8 is a schematic diagram of another sample processing device according to an embodiment of the present application;

[0025] Fig. 9 is a schematic diagram of a sample processing device according to an embodiment of the present application;

[0026] Fig.10 It is a structural block diagram of a computer terminal according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] The technical solution provided in this application is mainly implemented by large-scale model technology. The large model here refers to a deep learning model with large-scale model parameters, which can usually contain hundreds of millions, tens of billions, hundreds of billions, trillions or even more than ten trillion model parameters. The large model can also be called a cornerstone model / foundation model (Foundation Model). The large model is pre-trained through large-scale unlabeled corpus to produce a pre-trained model with more than 100 million parameters. This model can adapt to a wide range of downstream tasks, and the model has good generalization ability, such as large-scale language model (Large Language Model, LLM for short), multi-modal pre-training model (multi-modal pre-training model), etc.

[0030] It should be noted that when the large model is actually applied, the pre-trained model can be fine-tuned through a small number of samples so that the large model can be applied to different tasks. For example, the large model can be widely used in natural language processing (NLP), computer vision, speech processing and other fields, specifically in computer vision tasks such as visual question answering (VQA), image caption (IC), image generation, etc. It can also be widely used in natural language processing tasks such as text-based sentiment processing, text summary generation, machine translation, etc. Therefore, the main application scenarios of the large model include but are not limited to digital assistants, intelligent robots, search, online education, office software, e-commerce, intelligent design, etc.

[0031] Example 1

[0032] According to an embodiment of the present application, a sample processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] Considering the huge number of model parameters of the large model and the limited computing resources of the mobile terminal, the processing method of the above sample provided in the embodiment of the present application can be applied to Figure 1 The application scenarios shown are not limited to these. Figure 1 is a schematic diagram of an application scenario of a sample processing method according to an embodiment of the present application. Figure 1In the application scenario shown, the large model is deployed in the server 10, and the server 10 can be connected to one or more client devices 20 through a local area network connection, a wide area network connection, an Internet connection, or other types of data networks. The client device 20 here may include but is not limited to: a smart phone, a tablet computer, a laptop computer, a PDA, a personal computer, a smart home device, a vehicle-mounted device, etc. The client device 20 can interact with the user through a graphical user interface to implement the call of the large model, thereby implementing the method provided in the embodiment of the present application.

[0034] In an embodiment of the present application, a system composed of a client device and a server can perform the following steps: the client device executes to obtain the current sample set for training the model to be processed. The server executes step S101 to determine the model to be processed; step S102, uses the large model to analyze the current sample set of the model to be processed, and generates prompt information, wherein the current sample set is used to train the model to be processed, and the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; step S103, at least control the large model to generate abnormal monitoring samples matching the prompt information, wherein the abnormal monitoring samples meet the rules; step S104, at least mark the abnormal monitoring samples, and generate enhanced samples, wherein the enhanced samples at least include enhanced samples matching the abnormal monitoring samples, and step S105, use the enhanced samples as samples of the sample set, train the trained model to be processed, and obtain the target model. It should be noted that when the operating resources of the client device can meet the deployment and operating conditions of the large model, the embodiment of the present application can be performed in the client device.

[0035] Under the above operating environment, this application provides Figure 2 The samples shown were processed as described above. Figure 2 FIG. 1 is a flow chart of a sample processing method according to an embodiment of the present application. Figure 2 As shown, the method may include the following steps:

[0036] Step S201, obtaining a current sample set for training a model to be processed.

[0037] In the technical solution provided in the above step S201 of the present application, the current sample set may include a small number of initial samples for training the model to be processed, that is, the current sample set may be a small sample data set. The model to be processed may be a small model that has not been trained.

[0038] In this embodiment, query information can be generated according to the task to be performed by the model to be processed, and the query information is used to request the generation of the current sample set to train the model to be processed. The query information is multimodal information, and the type of the multimodal information includes at least one of the following: text information including character information, video frame information including frame image information, and audio information.

[0039] For example, if the task to be performed by the model to be processed is an image classification task, the query information may be image information. If the task to be performed by the model to be processed is a text emotion recognition task, the query information may be text information. After the query information is generated, the current sample set for training the model to be processed may be collected from a public data set, an open data source, or the Internet based on the query information. It should be noted that the specific form of the query information is not limited here.

[0040] Optionally, after obtaining the current sample set, the samples in the current sample set can be further divided into initial correct samples and initial error samples according to the model to be processed. The initial correct samples can be samples marked as normal, which can be recorded as Dcorrect samples, and the initial error samples can be samples marked as abnormal, which can be recorded as Dwrong samples.

[0041] For example, the initial model may be a small model that has not been trained and is in an initial state, and the current sample set may be a data set that contains only a small number of samples, that is, a small sample data set. The initial correct samples in the current sample set may be samples that the initial model outputs correct execution results when the model is trained, and the initial error samples in the current sample set may be samples that the initial model outputs error execution results when the model is trained. The current sample set may be marked as initial correct samples and initial error samples based on the execution results of the initial model.

[0042] Step S202: Use the large model to analyze the current sample set to obtain prompt information.

[0043] In the technical solution provided in the above step S202 of the present application, after obtaining the current sample set, the large model can be used to analyze the current sample set to obtain prompt information. That is, the large model is used to analyze the initial error samples and the initial correct samples in the current sample set to obtain prompt information, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set.

[0044] In this embodiment, since the current sample set is a data set containing only a small number of samples, the small model in the initial state is trained using the current sample set. Due to the limited amount of training data, the small model cannot be trained well. Based on this, the initial correct samples and initial error samples in the current sample set can be analyzed using the large model to determine the law of the small model making mistakes, and then obtain prompt information. Among them, the prompt information can be an intermediate output result in the large model enhancement processing process. Since the prompt information is used to characterize the rules satisfied by the samples marked as abnormal states in the current sample set, that is, it can be the rules satisfied by the initial error samples, so that after obtaining the prompt information, the large model can enhance the current sample set based on the prompt information, so as to obtain the enhanced sample data. The enhanced sample data can be used to train the model to be processed in a targeted manner. It should be noted that the prompt information can be a prompt text or a guiding information, which is used to prompt the large model to actively learn to generate an enhanced sample data set.

[0045] For example, using the big model to analyze the initial erroneous samples in the current sample set can enable the big model to summarize the patterns of the small model's errors from the samples that the small model predicted incorrectly. Using the big model to analyze the initial correct samples in the current sample set can enable the big model to reversely search for patterns from the samples that the small model predicted correctly, which helps the big model summarize more accurate patterns and generate more accurate prompt information.

[0046] Optionally, the current sample set is analyzed using a large model, and the resulting prompt information may include, in addition to the rules satisfied by the samples in the current sample set that are marked as abnormal, the rules satisfied by the samples in the current sample set that are marked as normal, that is, the rules satisfied by the samples that are correctly predicted by the model to be processed.

[0047] Step S203, controlling the large model to generate anomaly monitoring samples that match the prompt information.

[0048] In the technical solution provided in the above step S203 of the present application, after obtaining the prompt information, the large model can perform active learning based on the prompt information, thereby generating anomaly monitoring samples that match the prompt information, wherein the anomaly monitoring samples satisfy the rules satisfied by the samples marked as abnormal states in the current sample set introduced above.

[0049] In this embodiment, the large model can actively learn based on the prompt information to generate abnormal monitoring samples. For example, as can be seen from the above introduction, the prompt information can be the rules satisfied by the samples marked as abnormal in the current sample set. Therefore, the large model can generate some sample data that satisfy the rule based on the rule, and the generated sample data is the abnormal monitoring sample.

[0050] The target correct samples and target error samples are predicted, among which the target correct samples are samples that the initial model predicted by the large model may classify correctly, and the target correct samples can be recorded as Deasy samples; the target error samples are samples that the initial model predicted by the large model may classify incorrectly, and the target error samples can be recorded as Dhard samples. Based on this, the prompt information can be displayed on the interactive interface for learning, and the target error samples for targeted training of the initial model are predicted.

[0051] Optionally, this embodiment can display the target error samples and target correct samples predicted by learning the prompt information on the interactive interface, which is not limited here.

[0052] Step S204: Mark the abnormal monitoring samples to generate enhanced samples.

[0053] In the technical solution provided in the above step S204 of the present application, after the abnormal monitoring sample data is generated, the abnormal monitoring sample data can be marked to generate enhanced samples, wherein the enhanced samples at least include enhanced samples that match the abnormal monitoring samples.

[0054] In this embodiment, after obtaining the abnormal monitoring samples, the large model can also mark the abnormal monitoring samples as target correct samples and target error samples according to the error rules of the model to be processed. Among them, the target correct samples are samples that the model to be processed may classify correctly as predicted by the large model, and the target correct samples can be recorded as Deasy samples; the target error samples are samples that the model to be processed may classify incorrectly as predicted by the large model, and the target error samples can be recorded as Dhard samples. Afterwards, the model to be processed can be trained using at least the target error samples. Among them, the target correct samples are samples that the model to be processed may classify correctly as predicted by the large model, and the target error samples are samples that the model to be processed may classify incorrectly as predicted by the large model.

[0055] For example, since the target error samples are samples that may be misclassified by the model to be processed obtained by the large model prediction, that is, samples that are difficult for the model to be processed, using the target error samples to train the model to be processed can enable the model to be processed to quickly make up for the current weaknesses. Based on this, at least the target error samples can be used for targeted training of the model to be processed to make up for the weaknesses of low prediction accuracy and large loss of the model to be processed, and avoid training the model to be processed on some better samples that have already been learned, thereby improving the training efficiency of the model.

[0056] Optionally, since the target correct samples are samples that the model to be processed may classify correctly after being predicted by the large model, that is, samples that are simple for the model to be processed, using the target correct samples to train the model to be processed can enable the model to be processed to reversely search for patterns from the target correct samples to summarize more accurate patterns and improve the accuracy of task execution. Based on this, in this embodiment, the target error samples and the target correct samples can be used simultaneously to train the model to be processed, so that the model to be processed is trained based on the target error samples to improve prediction accuracy and reduce losses (for example, loss), and is also trained based on the target correct samples to reversely search for patterns from the target correct samples to summarize more accurate patterns and improve the accuracy of task execution by the model to be processed.

[0057] Based on the steps S201 to S204 of the above embodiment, the samples in the current sample set can be analyzed by the large model to obtain prompt information, which is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set, that is, the rules satisfied by the samples predicted incorrectly by the model to be processed. Afterwards, the large model can be controlled to generate abnormal monitoring samples matching the prompt information according to the prompt information, and the abnormal monitoring samples can be marked to generate enhanced samples, which at least include enhanced samples matching the abnormal monitoring samples. Since the abnormal monitoring samples meet the rules satisfied by the samples predicted incorrectly by the model to be processed, based on this, when the enhanced samples are used to train the model to be processed, the model to be processed can be trained in a targeted manner, so that the model to be processed learns the samples predicted incorrectly before, and avoids training on useless samples, thereby achieving the technical effect of using the large model to generate effective training samples in a few-sample scenario, thereby improving the performance of the model to be processed in a few-sample scenario, thereby solving the technical problem of difficulty in generating effective samples.

[0058] The above method of this embodiment is further introduced below.

[0059] As an optional implementation, step S202, using a large model to analyze the current sample set to obtain prompt information, includes: using the large model to analyze samples marked as abnormal states and samples marked as normal states in the current sample set to obtain prompt information.

[0060] In this embodiment, the samples marked as abnormal in the current sample set can be the samples predicted correctly by the model to be processed, and the samples marked as normal in the current sample set can be the samples predicted incorrectly by the model to be processed. Based on this, the large model is used to analyze the samples marked as abnormal and the samples marked as normal in the current sample set, and the obtained prompt information can be used to characterize the rules satisfied by the samples marked as abnormal in the current sample set, that is, the rules satisfied by the samples predicted incorrectly by the model to be processed.

[0061] For example, after obtaining the current sample set, the model to be processed can be used to perform tasks on the samples in the current sample set, and based on the execution results of the model to be processed, the samples that are misclassified by the model to be processed are marked as samples in abnormal states, that is, initial error samples, and the samples that are correctly classified by the model to be processed are marked as samples in normal states, that is, initial correct samples. Afterwards, the large model is used to analyze the initial error samples and the initial correct samples to obtain the rules of the errors predicted by the model to be processed and the rules of the correct predictions of the model to be processed.

[0062] Optionally, when marking samples in the current sample set as samples in abnormal states and samples in normal states, they can be marked by adding labels, wherein the specific content of the label can be determined according to the task performed by the model to be processed, that is, the label is associated with the task performed by the model to be processed.

[0063] For example, in an image classification task, an incorrectly classified image can be marked as "error" or a specific suffix, such as "_error", can be added to its file name. A correctly classified image can be marked as "correct" or a specific suffix, such as "_right", can be added to its file name to distinguish it.

[0064] For another example, in a sentiment classification task, text samples with positive sentiment can be marked as "positive" or a specific suffix, such as "_positive", can be added to their file names. Text samples with negative sentiment can be marked as "negative" or a specific suffix, such as "_negative", can be added to their file names to distinguish them.

[0065] Next, a method of using the model to be processed to perform tasks on the current sample set to mark initial correct samples and initial error samples according to the execution results is further introduced.

[0066] In this embodiment, when the model to be processed is used to perform tasks on samples in the current sample set to mark initial error samples and initial correct samples, the samples in the current sample set can be input into the model to be processed to perform the task. In response to the model to be processed outputting an error execution result, the sample is marked as an initial error sample (e.g., Dwrong sample). Similarly, in response to the model to be processed outputting a correct execution result, the sample is marked as an initial correct sample (e.g., Dcorrect sample). The task performed by the model to be processed can be a classification task or an entity recognition task, which is not specifically limited here.

[0067] For example, taking image classification as an example, the classification goal is to correctly classify pictures of cats and dogs. Suppose a certain picture shows a cat. Input the picture into the model to be processed. If the execution result of the model to be processed is that the picture shows a dog, then the sample can be marked as an initial wrong sample. Similarly, suppose a certain picture shows a dog. Input the sample into the model to be processed. If the execution result of the model to be processed is that the picture shows a dog, then the sample can be marked as an initial correct sample. Through the above method, the samples in the current sample set can be marked as initial correct samples and initial wrong samples.

[0068] In this embodiment, after marking the samples in the current sample set as initial correct samples and initial error samples, the large model can be used to analyze the initial correct samples and initial error samples in the current sample set to obtain prompt information. Next, the process of using the large model to analyze the samples in the current sample set to obtain prompt information is further introduced.

[0069] As an optional implementation, a large model is used to analyze samples marked as abnormal states and samples marked as normal states in the current sample set to obtain prompt information, including: using the large model to learn samples marked as abnormal states and samples marked as normal states respectively to obtain first grammatical information and second grammatical information, wherein the first grammatical information is used to represent the grammatical rules satisfied by samples marked as abnormal states, and the second grammatical information is used to represent the grammatical rules satisfied by samples marked as normal states; using the second grammatical information to adjust the first grammatical information; and generating prompt information from the adjusted first grammatical information.

[0070] In this embodiment, after obtaining the samples marked as abnormal and the samples marked as normal in the current sample set, the large model can be used to actively learn the samples marked as abnormal and the samples marked as normal in the current sample set, so as to summarize the regularity of the samples marked as abnormal and the samples marked as normal in the current sample set, and obtain the first grammatical information satisfied by the samples marked as abnormal, and the second grammatical information satisfied by the samples marked as normal. The first grammatical information and the second grammatical information can be expressed in text form.

[0071] For example, taking the sentiment classification task as an example, assuming that the samples in the current sample set are some movie reviews, among which the samples marked as abnormal in the current sample set are samples whose actual sentiment label is "positive" in the movie reviews, but are predicted as "negative" by the model to be processed. For example, "I like this movie, it is a sad and heartwarming story". The samples marked as normal in the current sample set are samples whose actual sentiment label is "positive" in the movie reviews, and the prediction result of the model to be processed is also "positive". Based on this, after the large model learns the samples marked as abnormal in the current sample set, the first grammatical information obtained can be the grammar satisfied by sentences containing a mixture of positive and negative language, that is, sentences with mixed emotions. The first grammatical information is used to represent the rules satisfied by the samples marked as abnormal in the current sample set. Similarly, after the large model learns the samples marked as normal in the current sample set, the second grammatical information obtained can be the grammar satisfied by sentences containing a single emotional state, and the second grammatical information is used to represent the rules satisfied by the samples marked as normal in the current sample set.

[0072] Optionally, after obtaining the rule information, the large model may adjust the first grammatical information using the second grammatical information, and generate prompt information according to the adjusted first grammatical information.

[0073] For example, since the second grammatical information is used to represent the rules satisfied by the samples marked as normal in the current sample set, that is, the rules satisfied by the samples predicted correctly by the model to be processed, based on this, the rules can be reversely searched from the samples predicted correctly by the model to be processed according to the second grammatical information, and the first grammatical information can be adjusted to allow the large model to summarize more accurate rules for the errors made by the model to be processed.

[0074] Optionally, after adjusting the first grammatical information, the large model may generate prompt information according to the first grammatical information. For example, the first grammatical information is used as prompt information, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set, that is, the rules of the errors made by the model to be processed.

[0075] As an optional implementation, step S203, controlling the large model to generate abnormal monitoring samples that match the prompt information, includes: determining at least one sample type of the abnormal monitoring samples to be generated; controlling the large model to obtain a first initial monitoring sample set that meets the sample type; controlling the large model to determine the samples in the first initial monitoring sample set that meet the prompt information as abnormal monitoring samples.

[0076] In this embodiment, in order to prevent the large model from acquiring samples that are irrelevant to the task to be performed by the model to be processed, at least one sample type of the abnormal monitoring sample to be generated can be determined first, wherein the sample type can be a movie review or a book review, and no specific limitation is made here. After determining at least one sample type of the abnormal monitoring sample to be generated, the large model can be controlled to acquire a first initial monitoring sample set that meets the sample type.

[0077] For example, assuming that at least one sample type of the abnormal monitoring samples to be generated is a movie review, based on this, the big model can be controlled to obtain movie reviews from public data sets, open data sources or the Internet to form a first initial monitoring sample set. Afterwards, the big model is controlled to match the samples in the first initial monitoring sample set with the prompt information, and the samples in the first initial monitoring sample set that meet the prompt information are determined as abnormal monitoring samples.

[0078] Next, the process of controlling the large model to determine abnormal monitoring samples from the first initial monitoring sample set is further introduced.

[0079] As an optional implementation, the control large model determines the samples in the first initial monitoring sample set that meet the prompt information as abnormal detection samples, including: controlling the large model to match the samples in the first initial monitoring sample set with the prompt information; and determining the samples in the first initial monitoring sample set that successfully match the prompt information as abnormal monitoring samples.

[0080] In this embodiment, as can be seen from the above introduction, the prompt information is the rule satisfied by the sample marked as abnormal state. Based on this, after obtaining the first initial monitoring sample set, the sample that satisfies the prompt information in the first initial monitoring sample set can be determined according to the prompt information, and the sample can be determined as an abnormal detection sample.

[0081] For example, assume that the samples included in the first initial monitoring sample set obtained may include: "The ending is disappointing, but it is a great movie overall", "The movie is really interesting and made me laugh all the time", etc. Since the prompt information is the rule satisfied by the sample marked as abnormal, that is, the first grammatical information satisfied by the sample predicted incorrectly by the model to be processed, and it can be seen from the above introduction that the first grammatical information can be a sentence containing a mixture of positive and negative language, based on this, the samples that match the first grammatical information in the first initial monitoring sample set can be determined. For example, the sample "The ending is disappointing, but it is a great movie overall" contains both positive and negative language, based on this, the sample can be determined as an abnormal monitoring sample. According to this method, all abnormal monitoring samples in the first initial monitoring sample set can be determined. For the convenience of explanation, the abnormal monitoring sample can be called a Dhard sample.

[0082] As an optional implementation, the sample processing method also includes: determining a first sample quantity of abnormal monitoring samples to be generated; controlling the large model to determine the samples that meet the prompt information in the first initial monitoring sample set as abnormal monitoring samples, including: controlling the large model to determine a second initial monitoring sample set that meets the prompt information in the first initial monitoring sample set, and in the second initial monitoring sample set, determining the samples with the first sample quantity as abnormal monitoring samples.

[0083] In this embodiment, in order to avoid an excessive amount of data, the first sample quantity of the abnormal monitoring samples to be generated can be determined first. Afterwards, the control model determines the second initial monitoring sample set that meets the prompt information in the first initial monitoring sample set. Since the second initial monitoring sample set includes a large number of abnormal monitoring samples that meet the prompt information, based on this, the first sample quantity of abnormal monitoring samples can be obtained in the second initial monitoring sample set.

[0084] For example, the first sample quantity can be preset, for example, the first sample quantity can be 2 or 4, and there is no specific limitation here. The specific implementation method of the control large model determining the second initial monitoring sample set that meets the prompt information in the first initial monitoring sample set can refer to the method of the control large model determining the samples that meet the prompt information in the first initial monitoring sample set introduced above, which will not be repeated here. Assuming that the acquired second initial monitoring sample set contains 10 samples, and the 10 samples all meet the prompt information, based on this, the samples of the first sample quantity can be selected from the 10 samples as abnormal monitoring samples.

[0085] As an optional implementation, step S204, marking the abnormal monitoring samples to generate enhanced samples, includes: marking the abnormal monitoring samples using a large model to obtain a first label of the abnormal monitoring samples; and generating enhanced samples based on the abnormal monitoring samples associated with the first label.

[0086] In this embodiment, after obtaining the abnormal monitoring sample, the large model can also be used to label the abnormal monitoring sample, that is, add a label to the abnormal monitoring sample, and then obtain the first label of the abnormal monitoring template, and then generate an enhanced sample from the abnormal detection sample labeled by the first label. The label can be associated with the task performed by the model to be processed.

[0087] For example, assuming that the task to be performed by the model to be processed is a sentiment classification task, after obtaining the abnormal monitoring samples, the labels used by the large model to mark the abnormal monitoring samples can be "positive" or "negative". For example, the abnormal monitoring samples may include: "The ending is disappointing, but it is a great movie overall", "The beginning of the book is slow and boring, but the warm ending makes up for it", and after the large model analyzes the above samples, it marks each sample, and the first labels obtained can be "positive" and "negative" respectively.

[0088] Optionally, after obtaining the first label of the anomaly detection sample, an enhanced sample may be generated based on the anomaly monitoring sample associated with the first label, wherein the enhanced sample at least includes an enhanced sample that matches the anomaly detection sample.

[0089] Optionally, the above describes how to control the large model to generate abnormal monitoring samples that match the prompt information and add labels to the abnormal monitoring samples. It should be noted that the large model can not only generate abnormal detection samples that match the prompt information, but also generate normal monitoring samples that do not match the prompt information. The following is a further introduction to the process of controlling the large model to generate normal monitoring samples that do not match the prompt information.

[0090] As an optional implementation, the sample processing method also includes: controlling the large model to generate normal monitoring samples that do not match the prompt information; using the large model to mark the normal monitoring samples to obtain a second label for the normal monitoring samples; generating enhanced samples based on the abnormal monitoring samples associated with the first label, including: determining the abnormal monitoring samples associated with the first label and the normal monitoring samples associated with the second label as enhanced samples, wherein the enhanced samples also include enhanced samples that match the normal monitoring samples.

[0091] In this embodiment, at least one sample type of the normal monitoring sample to be generated can be determined first, which is consistent with the at least one sample type of the abnormal monitoring sample to be generated as described above. The at least one sample type of the normal monitoring sample can also be a book review or a movie review, etc., which is not specifically limited here. After determining at least one sample type of the normal monitoring sample to be generated, the large model can be controlled to obtain samples consistent with the at least one sample type from public data sets, open data sources or the Internet. Afterwards, the samples that do not meet the prompt information among the obtained samples are determined as normal monitoring samples.

[0092] For example, assuming that at least one sample type of the normal monitoring samples to be generated is a movie review, the samples obtained by the large model based on at least one sample type of the normal monitoring samples to be generated may include: "The ending is disappointing, but overall it is a great movie", "This movie is very funny and made me laugh all the time", "The plot is boring and does not develop well". As can be seen from the above introduction, the prompt information is the rules satisfied by the samples marked as abnormal, that is, the first grammatical information satisfied by the samples predicted incorrectly by the model to be processed, and the first grammatical information may be a sentence containing a mixture of positive and negative language. Based on this, it can be determined that the samples that do not satisfy the prompt information in the above samples are: "This movie is very funny and made me laugh all the time", "The plot is boring and does not develop well". Based on this, the samples that do not satisfy the prompt information can be determined as normal monitoring samples, wherein the normal monitoring samples are samples that may be predicted correctly by the model to be processed.

[0093] Optionally, after the normal monitoring samples are determined, the large model may be controlled to label the normal monitoring samples to obtain a second label for the normal monitoring samples.

[0094] For example, assuming that the task performed by the model to be processed is a sentiment classification task, after obtaining the normal monitoring samples, the labels used to mark the normal monitoring samples using the large model can be "positive" or "negative". For example, the second label for marking the normal monitoring sample "The ending is disappointing, but overall it is a great movie" can be "positive", and the second label for marking the normal monitoring sample "The beginning of the book is slow and boring, but the warm ending makes up for it" can be "positive". According to this method, each sample in the normal monitoring samples can be marked with a second label.

[0095] Optionally, after marking the abnormal monitoring sample with a first label and marking the normal monitoring sample with a second label, the abnormal monitoring sample associated with the first label and the normal monitoring sample associated with the second label can be determined as enhanced samples, that is, the enhanced samples include not only enhanced samples that match the abnormal detection samples, but also enhanced samples that match the normal monitoring samples.

[0096] As an optional implementation, the sample processing method further includes: using abnormal monitoring samples and normal monitoring samples as training sets for the model to be processed; and using the samples in the training set to train the model to be processed into a target model.

[0097] In this embodiment, after obtaining the abnormal detection samples and the normal monitoring samples, the abnormal monitoring samples and the normal monitoring samples can be used as the training set of the model to be processed, and the samples of the training set can be used to train the model to be processed. Among them, since the abnormal monitoring samples are samples that may be misclassified by the model to be processed obtained by the large model prediction, that is, samples that are difficult for the model to be processed, the abnormal monitoring samples can be used to conduct targeted training on the model to be processed, so that the model to be processed can quickly make up for the weaknesses of low prediction accuracy and large losses. The normal monitoring samples are samples that may be correctly classified by the model to be processed obtained by the large model prediction, that is, samples that are simple for the model to be processed. Using the normal monitoring samples to train the model to be processed can enable the model to be processed to reversely search for rules from the normal monitoring samples, so as to summarize more accurate rules and improve the accuracy of task execution.

[0098] In the above operating environment, this application also provides Figure 3 The samples shown were processed as described above. Figure 3 FIG. 1 is a flow chart of another sample processing method according to an embodiment of the present application. Figure 3 As shown, the method may include the following steps.

[0099] Step S301, responding to inquiry information received in the dialogue interface.

[0100] In the technical solution provided in the above step S301 of the present application, the user can input inquiry information in the dialogue interface according to the needs, wherein the inquiry information is used to request enhancement processing of the current sample set, and the current sample set is used to train the model to be processed. The inquiry information can be multimodal information, and the multimodal information can be text information containing character information, video frame information containing frame image information, audio information, etc. The model to be processed can be a small model in the initial state.

[0101] Step S302: Display the current sample set in the dialogue interface.

[0102] In the technical solution provided in the above step S302 of the present application, the current sample set may include a small number of initial samples for training the model to be processed, that is, the current sample set may be a small sample data set. The model to be processed may be a small model that has not been trained. The current sample set may be obtained according to the task to be performed by the model to be processed, and after the current sample set is obtained, the current sample set may be displayed in the dialogue interface.

[0103] Step S303: Use the large model to analyze the current sample set, and display the generated prompt information on the dialogue interface.

[0104] In the technical solution provided in the above step S303 of the present application, after obtaining the current sample set, a large model can be used to analyze the samples in the current sample set, and the generated prompt information can be displayed on the dialogue interface, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set.

[0105] In this embodiment, since the current sample set is a data set that only contains a small number of samples, the current sample set is used to train the model to be processed. Due to the limited amount of training data, the performance of the model to be processed cannot be well trained. Based on this, a large model can be used to analyze the samples in the current sample set to obtain the rules satisfied by the samples marked as abnormal states in the current sample set, that is, the rules satisfied by the samples predicted incorrectly by the model to be processed.

[0106] For example, the model to be processed can be used to perform tasks on the current sample set first, and according to the execution results of the model to be processed, the samples in the current sample set can be marked as samples in abnormal state and samples in normal state, wherein the samples marked as abnormal state are samples corresponding to the incorrect execution results of the task execution results of the model to be processed, and the samples marked as normal state are samples corresponding to the correct execution results of the task execution results of the model to be processed. The samples marked as abnormal state in the current sample set are analyzed using the large model to obtain the rules satisfied by the samples in abnormal state, the samples marked as normal state in the current sample set are analyzed using the large model to obtain the rules satisfied by the samples in normal state, and the rules satisfied by the samples in normal state are further adjusted using the rules satisfied by the samples in abnormal state to obtain prompt information, which is used to characterize the rules satisfied by the samples marked as abnormal state in the current sample set.

[0107] Step S304: display at least the abnormal monitoring samples matching the prompt information on the dialogue interface.

[0108] In the technical solution provided in the above step S304 of the present application, after obtaining the abnormal monitoring samples, at least the abnormal monitoring samples matching the prompt information can be displayed on the dialogue interface, wherein the abnormal monitoring samples are generated based on the large model.

[0109] Optionally, abnormal monitoring samples that match the prompt information and normal monitoring samples that do not match the prompt information can be displayed in the dialogue interface, and no specific limitation is made here.

[0110] Step S305: displaying, on the dialogue interface, at least an enhanced sample generated by marking the abnormal monitoring sample.

[0111] In the technical solution provided in the above step S305 of the present application, after marking the abnormal monitoring sample and generating the enhanced sample, at least the enhanced sample generated by marking the abnormal monitoring sample can also be displayed on the dialogue interface. The process of marking the abnormal monitoring sample and generating the enhanced sample can refer to the method of marking the first label of the abnormal detection sample introduced above, which will not be repeated here.

[0112] Step S306: Based on the enhanced sample, response information corresponding to the inquiry information is generated and displayed on the dialogue interface.

[0113] In the technical solution provided in the above step S306 of the present application, since the inquiry information is used to enhance the current sample set, based on this, reply information corresponding to the inquiry information can be generated according to the enhanced sample and displayed on the dialogue interface. Among them, the inquiry information can be multimodal information, and the type of multimodal information includes at least one of the following: text information containing character information, video frame information containing frame image information, and audio information. The type of reply information includes at least one of the following: text information, image information, video information, and voice information.

[0114] Based on the steps S301 to S306 of the above embodiment, the samples in the current sample set can be analyzed by the large model to generate prompt information, which is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set, and the current sample set is enhanced according to the prompt information to obtain abnormal monitoring samples, and the abnormal monitoring samples are marked to obtain enhanced samples. Since the enhanced samples at least include enhanced samples that match the abnormal monitoring samples, the enhanced error samples can be used to train the model to be processed in a targeted manner, so that the model to be processed learns the samples that were previously predicted to be wrong, avoiding training on useless samples, and achieving the technical effect of using a large model to generate effective training samples in a few-sample scenario, thereby improving the performance of the initial model in a few-sample scenario, thereby solving the technical problem of difficulty in generating effective samples.

[0115] The above method of this embodiment is further elaborated below.

[0116] As an optional implementation, the present application may modify the prompt information by responding to a modification operation performed on the interactive interface.

[0117] In this embodiment, after the large model analyzes the samples in the current sample set and obtains the prompt information, the prompt information can be displayed on the interactive interface. When responding to the modification operation on the prompt information, the prompt information can be modified to obtain prompt information that better meets the user's standards. The modification operation can be operations such as adding data, deleting data, and updating data, which are not specifically limited here.

[0118] As another optional implementation, the present application may also update the current sample set using abnormal monitoring samples by responding to a sample update operation on the interactive interface.

[0119] In this embodiment, after the abnormal monitoring samples and normal monitoring samples are generated using the large model, the generated abnormal monitoring samples and normal monitoring samples can be used to update the samples in the current sample set to continuously generate new data and avoid the monotony of the model training data. According to this method, the data can be continuously enhanced to improve the effectiveness of model training.

[0120] As another optional implementation, the present application may also modify the abnormal detection samples and / or normal monitoring samples by responding to modification operations on the abnormal detection samples and / or normal monitoring samples on the interactive interface.

[0121] In this embodiment, the interaction interface may also display abnormal detection samples and / or normal monitoring samples, based on which the user may also modify and adjust the abnormal detection samples and / or normal monitoring samples displayed on the interaction interface so that the abnormal detection samples and / or normal monitoring samples better meet the user's requirements for training the initial model. The second modification operation may be operations such as adding data, deleting data, and updating data, which are not specifically limited here.

[0122] It can be seen from the scheme implemented in the above steps that the present application can modify the content displayed on the interactive interface so that the obtained abnormal monitoring samples and normal monitoring samples are more in line with user requirements, ensuring that the processing model is trained according to the abnormal monitoring samples and normal monitoring samples, and the obtained target model is more in line with user requirements.

[0123] According to an embodiment of the present application, a sample processing method is also provided. Figure 4 FIG. 1 is a flow chart of another sample processing method according to an embodiment of the present application. Figure 4As shown, the method may include the following steps:

[0124] Step S401, determining the model to be processed.

[0125] In the technical solution provided in the above step S401 of the present application, the model to be processed may be an untrained small model, that is, a small model in an initial state.

[0126] Step S402: Use the large model to analyze the current sample set of the model to be processed and generate prompt information.

[0127] In the technical solution provided in the above step S402 of the present application, the current sample set may be a data set containing only a small number of samples, that is, a small sample data set. After obtaining the current sample set, the current sample set of the model to be processed may be analyzed using a large model to generate prompt information. The current sample set is used to train the model to be processed, and the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set.

[0128] In this embodiment, the current sample set for training the model to be processed can be collected from public data sets, open data sources or the Internet according to the sample type of the sample corresponding to the task to be performed by the model to be processed. After obtaining the current sample set, the model to be processed can be used to perform tasks on the samples in the current sample set, and according to the task execution results of the model to be processed, the samples in the current sample set marked as abnormal and the samples marked as normal are determined, and then the large model is used to analyze the samples in the current sample set marked as abnormal and the samples marked as normal, and prompt information is obtained, wherein the prompt information is used to characterize the rules satisfied by the samples in the current sample set marked as abnormal.

[0129] Step S403, at least control the large model to generate anomaly monitoring samples that match the prompt information.

[0130] In the technical solution provided in the above step S403 of the present application, after obtaining the prompt information, the large model can at least be controlled to generate anomaly monitoring samples matching the prompt information, wherein the anomaly monitoring samples satisfy the rules satisfied by the samples marked as abnormal states.

[0131] Optionally, after obtaining the prompt information, the large model may be controlled to generate normal monitoring samples that do not match the prompt information, wherein the normal monitoring samples do not satisfy the rules satisfied by the samples marked as abnormal states.

[0132] Step S404: at least mark the abnormal monitoring samples to generate enhanced samples.

[0133] In the technical solution provided in the above step S404 of the present application, after obtaining the abnormal monitoring sample, the abnormal monitoring sample can be marked to generate an enhanced sample. Among them, the enhanced sample at least includes an enhanced sample that matches the abnormal monitoring sample. Among them, the method of marking the abnormal monitoring sample and generating the enhanced sample can refer to the method introduced in the above embodiment, which will not be repeated here.

[0134] Optionally, the normal detection sample may be marked to generate an enhanced sample, which includes an enhanced sample that matches the normal monitoring sample.

[0135] Step S405: Take the enhanced samples as samples of the sample set, train the trained model to be processed, and obtain the target model.

[0136] In the technical solution provided in the above step S405 of the present application, after obtaining the enhanced samples, the enhanced samples can be used as samples of the sample set to train the trained model to be processed, wherein the enhanced samples may include samples after the abnormal monitoring samples are marked, and may also include samples after the normal monitoring samples are marked. Since the abnormal monitoring samples are samples that may be misclassified by the model to be processed obtained by the large model prediction, that is, samples that are difficult for the model to be processed, the abnormal monitoring samples can be used to perform targeted training on the model to be processed, so that the model to be processed can quickly make up for the weaknesses of low prediction accuracy and large losses. The normal monitoring samples are samples that may be correctly classified by the model to be processed obtained by the large model prediction, that is, samples that are simple for the model to be processed. Using the normal monitoring samples to train the model to be processed can enable the model to be processed to reversely search for rules from the normal monitoring samples, so as to summarize more accurate rules and improve the accuracy of task execution.

[0137] Based on steps S401 to S405 of the above embodiment, the samples in the current sample set can be analyzed by a large model to generate prompt information, which is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set. The current sample set is enhanced according to the prompt information to obtain abnormal monitoring samples, and the abnormal monitoring samples are marked to obtain enhanced samples. Since the enhanced samples at least include enhanced samples that match the abnormal monitoring samples, the enhanced error samples can be used to train the model to be processed in a targeted manner, so that the model to be processed learns the samples that were previously predicted incorrectly, avoiding training on useless samples, and achieving the technical effect of generating effective training samples in a few-sample scenario using a large model, thereby improving the performance of the initial model in a few-sample scenario, thereby solving the technical problem of difficulty in generating effective samples.

[0138] Figure 5is a schematic diagram of a sample processing system according to an embodiment of the present application. Figure 5 As shown, the sample processing system 500 includes: a data recognition end 501, a data enhancement end 502 and a model training end 503.

[0139] The data identification terminal 501 is used to identify the current sample set of the model to be processed, wherein the current sample set is used to train the model to be processed.

[0140] The data enhancement end 502 is used to use the large model to analyze the current sample set to obtain prompt information, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; at least control the large model to generate abnormal monitoring samples that match the prompt information, wherein the abnormal monitoring samples satisfy the rules; at least mark the abnormal monitoring samples to generate enhanced samples, wherein the enhanced samples at least include enhanced samples that match the abnormal monitoring samples.

[0141] The model training section 503 is used to train the trained model to be processed by taking at least the enhanced sample as a sample of the sample set to obtain a target model.

[0142] The technical solution of the embodiment of the present application is further introduced below with examples in combination with the preferred implementation manner.

[0143] In low-resource scenarios, small models are usually limited by the amount of training data and cannot perform well. Although large models have strong task generalization capabilities, they have high inference and deployment costs and limited usage scenarios. The collaborative working method of large and small models can combine the advantages of each model and is widely favored.

[0144] At present, large models (for example, Chat GPT) can be used to generate targeted training data, that is, the large model is used to enhance the training sample set, and the enhanced data is used to train the small model. However, the enhanced data is fixed and not all of it is useful. There may be some redundant data, which makes the training of the small model repeated. There may also be a lack of some content that is useful for the training of the small model, which makes the small model unable to learn relevant content. There is still a technical problem of low model training efficiency.

[0145] For example, the samples enhanced by the large model may mention certain content repeatedly, but the small model has already learned these contents well. In this case, the enhanced samples are redundant. Alternatively, the samples enhanced by the large model may not involve certain knowledge or words, resulting in the small model not learning the relevant content and performing poorly on these contents.

[0146] However, an embodiment of the present application provides a sample processing method, which can analyze the samples in the current sample set through a large model to generate prompt information, and the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set. The current sample set is enhanced according to the prompt information to obtain abnormal monitoring samples, and the abnormal monitoring samples are marked to obtain enhanced samples. Since the enhanced samples at least include enhanced samples that match the abnormal monitoring samples, the enhanced error samples can be used to train the model to be processed in a targeted manner, so that the model to be processed learns the samples that were previously predicted incorrectly, avoiding training on useless samples, and achieving the technical effect of using a large model to generate effective training samples in a few-sample scenario, thereby improving the performance of the initial model in a few-sample scenario, thereby solving the technical problem of difficulty in generating effective samples.

[0147] Figure 6 is a schematic diagram of a sample processing and model training method according to an embodiment of the present application. Figure 6 As shown in the figure, it includes the sample recognition process, the large model active learning process and the model training process. Next, these processes are introduced respectively.

[0148] First, the sample differentiation process in the initial sample is further introduced.

[0149] In this embodiment, after obtaining a small number of samples, the small sample data can be input into the model to be processed to perform the corresponding classification task, and then the small sample data can be divided into correct samples and incorrect samples according to the execution result output by the model to be processed, wherein the model to be processed can be a small model. When the model to be processed outputs a correct classification result, the sample corresponding to the classification result can be marked as a correct sample, which can also be called a Dcorrect sample. When the model to be processed outputs an incorrect classification result, the sample corresponding to the classification result can be marked as an incorrect sample, which can also be called a Dwrong sample.

[0150] After obtaining the correct samples and the wrong samples, the large model can be used to actively learn the correct samples and the wrong samples to generate a sample set for training. Next, the active learning process of the large model is further introduced.

[0151] In this embodiment, the correct samples and the wrong samples can be input into the model, and the large model can be used to analyze the correct samples and the wrong samples to obtain the error pattern of the small model. For example, the small model performs poorly on samples with certain characteristics, has a large loss or a low prediction accuracy, and then outputs the error pattern of the model to be processed in the form of text.

[0152] Afterwards, the large model can perform data enhancement based on the wrong samples and correct samples according to the summarized rules to obtain enhanced sample data, and label the enhanced sample data to illustrate the specific characteristics of the sample. Among them, the specific form of the label is related to the classification task to be performed by the model to be processed. In the classification task, the label can be the corresponding label text. In other tasks, such as entity recognition, it can be the corresponding entity type, etc. The form of the label is not unique. For example, in the emotion recognition task, the label corresponding to the sample can be the emotion "positive", "negative", "with mixed emotions", etc.

[0153] Optionally, the sample data after data enhancement by the large model is divided into difficult samples and simple samples, wherein the difficult samples are samples that the to-be-processed model predicted by the large model may predict incorrectly, that is, abnormal monitoring samples, and the simple samples are samples that the to-be-processed model predicted by the large model may predict correctly, that is, normal monitoring samples. The difficult samples and simple samples are output, wherein the difficult samples can be samples that the to-be-processed model predicted by the large model may classify incorrectly; and the simple samples can be samples that the to-be-processed model predicted by the large model may classify correctly. In order to distinguish between correct samples that are actually correctly classified by the to-be-processed model (for example, Dcorrect samples) and incorrect samples that are actually incorrectly classified by the to-be-processed model (for example, Dwrong samples), the simple samples that the to-be-processed model predicted by the large model may classify correctly can be called Deasy samples, and the samples that the to-be-processed model predicted by the large model may classify incorrectly can be called Dhard samples.

[0154] Optionally, the difficult samples and simple samples predicted by the large model are used as batch data for training the model to be processed. The size of the batch data can be set to batch size, and the size of the difficult samples and the size of the simple samples are respectively set to batch size / 2.

[0155] After obtaining the batch data, the batch data can be used to train the model to be processed, that is, the model to be processed can be used to identify the batch data samples. The model training process is further introduced below.

[0156] In this embodiment, batch data can be input into the model to be processed, and then the samples correctly classified and incorrectly classified by the model to be processed are determined according to the loss value and prediction accuracy of the model to be processed. The samples correctly classified by the model to be processed are taken as Dcorrect samples to iterate the previous Dcorrect samples, and at the same time, the samples incorrectly classified by the model to be processed are taken as Dwrong samples to iterate the previous Dwrong samples. In this way, multiple rounds of data enhancement are performed on the initial samples, and then the enhanced data is used to train the model to be processed until the model to be processed achieves better performance in the task to be performed, and the model to be processed is used as the target model.

[0157] In this application, data enhancement does not generate all the data for training the small model at one time, but performs multiple update iterations, which ensures that the small model will not always be trained on the same sample. During the entire training process, the large model will continuously analyze the performance and weaknesses of the small model, and continuously generate new training data to avoid the monotony of the small model training data. In addition, this application uses the method of specifying active learning strategies by the large model to generate training sample data in a targeted manner, which can make up for the knowledge that the small model has not seen before. In addition, the samples of active learning enhancement of the large model can directly discover the weaknesses of the small model, and the generated prediction samples can avoid the small model from being trained on useless samples, thereby enhancing the training efficiency of the model.

[0158] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0159] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0160] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD), including several instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0161] Example 2

[0162] According to an embodiment of the present application, a sample processing device for implementing the above sample processing method is also provided. Figure 7 is a schematic diagram of a sample processing device according to an embodiment of the present application, such as Figure 7 As shown, the sample processing device 700 may include: an acquisition unit 701 , an analysis unit 702 , a first control unit 703 and a first marking unit 704 .

[0163] The acquisition unit 701 is used to acquire a current sample set for training the model to be processed.

[0164] The analyzing unit 702 is used to analyze the current sample set using the large model to obtain prompt information, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set.

[0165] The first control unit 703 is used to control the large model to generate anomaly monitoring samples that match the prompt information, wherein the anomaly monitoring samples satisfy the rules.

[0166] The first marking unit 704 is used to mark the abnormal monitoring sample to generate an enhanced sample, wherein the enhanced sample at least includes an enhanced sample that matches the abnormal monitoring sample.

[0167] It should be noted that the acquisition unit 701, the analysis unit 702, the control unit 703 and the first marking unit 704 correspond to steps S201 to S204 in Example 1, and the four units and the corresponding steps implement the same examples and application scenarios, but are not limited to the contents disclosed in the above-mentioned Example 1. It should be noted that the above-mentioned modules or units may be hardware components or software components stored in a memory and processed by one or more processors, and the above-mentioned modules may also be part of the device and may run in the server 10 provided in Example 1.

[0168] According to an embodiment of the present application, a sample processing device for implementing the above sample processing method is also provided. Figure 8 is a schematic diagram of another sample processing and generating device according to an embodiment of the present application, such as Figure 8 As shown, the sample processing device 800 may include: a receiving unit 801 , a first display unit 802 , a second display unit 803 , a third display unit 804 , a fourth display unit 805 and a first generating unit 806 .

[0169] The receiving unit 801 is used to respond to the inquiry information received in the dialogue interface, wherein the inquiry information is used to request enhancement processing of the current sample set, and the current sample set is used to train the model to be processed.

[0170] The first display unit 802 is used to display the current sample set in the dialogue interface.

[0171] The second display unit 803 is used to analyze the current sample set using the large model and display the generated prompt information on the dialogue interface, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set.

[0172] The third display unit 804 is used to display at least an abnormal monitoring sample matching the prompt information on the dialogue interface, wherein the abnormal monitoring sample is generated based on the large model.

[0173] The fourth display unit 805 is used to display, on the dialogue interface, an enhanced sample generated by marking at least the abnormal monitoring sample, wherein the enhanced sample at least includes an enhanced sample that matches the abnormal monitoring sample.

[0174] The first generating unit 806 is used to generate reply information corresponding to the inquiry information based on the enhanced sample and display it on the dialogue interface.

[0175] It should be noted that the receiving unit 801, the first display unit 802, the second display unit 803, the third display unit 804, the fourth display unit 805 and the first generating unit 806 correspond to steps S301 to S306 in Embodiment 1, and the six units and the corresponding steps implement the same examples and application scenarios, but are not limited to the contents disclosed in Embodiment 1. It should be noted that the modules or units may be hardware components or software components stored in a memory and processed by one or more processors, and the modules may also be part of a device and run in the server 10 provided in Embodiment 1.

[0176] According to an embodiment of the present application, a sample processing device for implementing the above sample processing method is also provided. Fig. 9 is a schematic diagram of a sample processing device according to an embodiment of the present application, such as Fig. 9As shown, the sample processing device 900 may include: a determination unit 901 , a second generation unit 902 , a second control unit 903 , a second marking unit 904 and a training unit 905 .

[0177] The determination unit 901 is used to determine the model to be processed.

[0178] The second generating unit 902 is used to use the large model to analyze the current sample set of the model to be processed and generate prompt information, wherein the current sample set is used to train the model to be processed, and the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set.

[0179] The second control unit 903 is used to at least control the large model to generate an abnormal monitoring sample that matches the prompt information, wherein the abnormal monitoring sample satisfies the rule.

[0180] The second marking unit 904 is used to mark at least the abnormal monitoring sample to generate an enhanced sample, wherein the enhanced sample at least includes an enhanced sample that matches the abnormal monitoring sample.

[0181] The training unit 905 is used to train the trained model to be processed by taking the enhanced samples as samples of the sample set to obtain a target model.

[0182] It should be noted here that the above-mentioned determination unit 901, second generation unit 902, second control unit 903, second marking unit 904 and training unit 905 correspond to steps S401 to S405 in Example 1, and the five units and the corresponding steps implement the same examples and application scenarios, but are not limited to the contents disclosed in the above-mentioned Example 1. It should be noted that the above-mentioned modules or units can be hardware components or software components stored in a memory and processed by one or more processors, and the above-mentioned modules can also be run in the server 10 provided in Example 1 as part of the device.

[0183] It should be noted that the preferred implementation scheme involved in the above embodiments of the present application is the same as the scheme provided in Example 1, as well as the application scenario and implementation process, but is not limited to the scheme provided in Example 1.

[0184] Example 3

[0185] The embodiment of the present application may provide a computer terminal, which may be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal may also be replaced by a terminal device such as a mobile terminal.

[0186] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of the computer network.

[0187] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the sample processing method: obtaining a current sample set for training a model to be processed; using a large model to analyze the current sample set to obtain prompt information, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; controlling the large model to generate abnormal monitoring samples that match the prompt information, wherein the abnormal monitoring samples satisfy the rules; marking the abnormal monitoring samples to generate enhanced samples, wherein the enhanced samples at least include enhanced samples that match the abnormal monitoring samples.

[0188] Optionally, Fig.10 is a structural block diagram of a computer terminal according to an embodiment of the present application. Fig.10 As shown, the computer terminal A may include: one or more (only one is shown in the figure) processors 1002, a memory 1004, a storage controller, and a peripheral interface, wherein the peripheral interface is connected to a radio frequency module, an audio module, and a display.

[0189] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the sample processing method and device in the embodiment of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned sample processing method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal A via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0190] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: obtain the current sample set for training the model to be processed; use the large model to analyze the current sample set to obtain prompt information, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; control the large model to generate abnormal monitoring samples that match the prompt information, wherein the abnormal monitoring samples satisfy the rules; mark the abnormal monitoring samples to generate enhanced samples, wherein the enhanced samples at least include enhanced samples that match the abnormal monitoring samples.

[0191] Optionally, the processor may also execute program code of the following steps: using the large model, analyzing samples marked as abnormal and samples marked as normal in the current sample set to obtain prompt information.

[0192] Optionally, the processor may also execute the program code of the following steps: using a large model to respectively study samples marked as abnormal states and samples marked as normal states to obtain first grammatical information and second grammatical information, wherein the first grammatical information is used to represent the grammatical rules satisfied by samples marked as abnormal states, and the second grammatical information is used to represent the grammatical rules satisfied by samples marked as normal states; using the second grammatical information to adjust the first grammatical information; and generating prompt information using the adjusted first grammatical information.

[0193] Optionally, the processor may also execute program codes of the following steps: determining at least one sample type of the abnormal monitoring samples to be generated; controlling the large model to obtain a first initial monitoring sample set that meets the sample type; and controlling the large model to determine the samples in the first initial monitoring sample set that meet the prompt information as abnormal monitoring samples.

[0194] Optionally, the processor may also execute program codes of the following steps: controlling the large model to match samples in the first initial monitoring sample set with prompt information; and determining samples in the first initial monitoring sample set that successfully match the prompt information as abnormal monitoring samples.

[0195] Optionally, the processor may also execute the program code of the following steps: determining a first sample quantity of abnormal monitoring samples to be generated; controlling the large model to determine a second initial monitoring sample set that meets the prompt information in the first initial monitoring sample set, and in the second initial monitoring sample set, determining samples with the first sample quantity as abnormal monitoring samples.

[0196] Optionally, the processor may also execute program codes of the following steps: labeling the abnormal monitoring samples using the large model to obtain a first label of the abnormal monitoring samples; and generating enhanced samples based on the abnormal monitoring samples associated with the first label.

[0197] Optionally, the processor may also execute program code of the following steps: controlling the large model to generate normal monitoring samples that do not match the prompt information; using the large model to mark the normal monitoring samples to obtain a second label for the normal monitoring samples; generating enhanced samples based on the abnormal monitoring samples associated with the first label, including: determining the abnormal monitoring samples associated with the first label and the normal monitoring samples associated with the second label as enhanced samples, wherein the enhanced samples also include enhanced samples that match the normal monitoring samples.

[0198] Optionally, the processor may also execute the program code of the following steps: using abnormal monitoring samples and normal monitoring samples as training sets for the model to be processed; and using the samples in the training set to train the model to be processed into a target model.

[0199] According to the embodiment of the present application, a method for processing samples is provided. The current sample set for training the model to be processed can be obtained first. Since the number of samples contained in the current sample set is small, based on this, the samples in the current sample set can be analyzed using a large model to obtain prompt information, which is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set, that is, the rules satisfied by the samples predicted incorrectly by the model to be processed. Afterwards, the large model can be controlled to generate abnormal monitoring samples matching the prompt information, and the abnormal monitoring samples can be marked to generate enhanced samples, which at least include enhanced samples matching the abnormal monitoring samples. Since the abnormal monitoring samples meet the rules satisfied by the samples predicted incorrectly by the model to be processed, based on this, when the enhanced samples are used to train the model to be processed, the model to be processed can be trained in a targeted manner, so that the model to be processed learns the samples predicted incorrectly before, and avoids training on useless samples, thereby achieving the technical effect of using a large model to generate effective training samples in a few-sample scenario, thereby improving the performance of the initial model in a few-sample scenario, thereby solving the technical problem of difficulty in generating effective samples.

[0200] It can be understood by those skilled in the art that Fig.10 The structure shown is for illustration only, and the computer terminal may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, or other terminal devices. Fig.10 It does not limit the structure of the above electronic device. For example, the computer terminal A may also include Fig.10 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Fig.10 Different configurations are shown.

[0201] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0202] Example 4

[0203] The embodiment of the present application further provides a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the sample processing method provided in the first embodiment.

[0204] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0205] Optionally, in this embodiment, the storage medium is configured to store program code for executing the following steps: obtaining a current sample set for training a model to be processed; using a large model to analyze the current sample set to obtain prompt information, wherein the prompt information is used to characterize the rules satisfied by the samples in the current sample set that are marked as abnormal; controlling the large model to generate abnormal monitoring samples that match the prompt information, wherein the abnormal monitoring samples satisfy the rules; marking the abnormal monitoring samples to generate enhanced samples, wherein the enhanced samples at least include enhanced samples that match the abnormal monitoring samples.

[0206] Optionally, the storage medium is further configured to store program codes for executing the following steps: using the large model, analyzing samples marked as abnormal and samples marked as normal in the current sample set to obtain prompt information.

[0207] Optionally, the storage medium is also configured to store program codes for executing the following steps: using a large model to respectively study samples marked as abnormal states and samples marked as normal states to obtain first grammatical information and second grammatical information, wherein the first grammatical information is used to represent the grammatical rules satisfied by samples marked as abnormal states, and the second grammatical information is used to represent the grammatical rules satisfied by samples marked as normal states; using the second grammatical information to adjust the first grammatical information; and generating prompt information using the adjusted first grammatical information.

[0208] Optionally, the storage medium is also configured to store program codes for executing the following steps: determining at least one sample type of the abnormal monitoring samples to be generated; controlling the large model to obtain a first initial monitoring sample set that meets the sample type; and controlling the large model to determine the samples in the first initial monitoring sample set that meet the prompt information as abnormal monitoring samples.

[0209] Optionally, the storage medium is also configured to store program codes for executing the following steps: controlling the large model to match samples in the first initial monitoring sample set with prompt information; and determining samples in the first initial monitoring sample set that successfully match the prompt information as abnormal monitoring samples.

[0210] Optionally, the storage medium is also configured to store program codes for executing the following steps: determining a first sample quantity of abnormal monitoring samples to be generated; controlling the large model to determine a second initial monitoring sample set that meets the prompt information in the first initial monitoring sample set, and in the second initial monitoring sample set, determining samples with the first sample quantity as abnormal monitoring samples.

[0211] Optionally, the storage medium is further configured to store program codes for executing the following steps: labeling the abnormal monitoring samples using the large model to obtain a first label of the abnormal monitoring samples; and generating enhanced samples based on the abnormal monitoring samples associated with the first label.

[0212] Optionally, the storage medium is further configured to store program codes for executing the following steps: controlling the large model to generate a normal monitoring sample that does not match the prompt information; using the large model to mark the normal monitoring sample to obtain a second label of the normal monitoring sample; generating an enhanced sample based on the abnormal monitoring sample associated with the first label, including: determining the abnormal monitoring sample associated with the first label and the normal monitoring sample associated with the second label as enhanced samples, wherein the enhanced sample also includes an enhanced sample that matches the normal monitoring sample. .

[0213] Optionally, the storage medium is also configured to store program codes for executing the following steps: using abnormal monitoring samples and normal monitoring samples as training sets for the model to be processed; and using samples in the training set to train the model to be processed into a target model.

[0214] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0215] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0216] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0217] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0218] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0219] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0220] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A sample processing method, characterized in that: include: Get the current sample set used to train the model to be processed; Using a large model to analyze the current sample set, to obtain prompt information, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; Controlling the large model to generate an abnormal monitoring sample matching the prompt information, wherein the abnormal monitoring sample satisfies the rule; The abnormal monitoring sample is marked to generate an enhanced sample, wherein the enhanced sample at least includes an enhanced sample that matches the abnormal monitoring sample.

2. The method according to claim 1, characterized in that The current sample set is analyzed using a large model to obtain prompt information, including: The large model is used to analyze the samples marked as abnormal and the samples marked as normal in the current sample set to obtain the prompt information.

3. The method according to claim 2, characterized in that Using the large model, analyzing the samples marked as abnormal and the samples marked as normal in the current sample set to obtain the prompt information includes: Using the large model to learn the samples marked as abnormal and the samples marked as normal, respectively, to obtain first grammatical information and second grammatical information, wherein the first grammatical information is used to represent the grammatical rules satisfied by the samples marked as abnormal, and the second grammatical information is used to represent the grammatical rules satisfied by the samples marked as normal; adjusting the first grammatical information using the second grammatical information; The adjusted first grammatical information is used to generate the prompt information.

4. The method according to claim 1, characterized in that Controlling the large model to generate an abnormal monitoring sample matching the prompt information includes: Determining at least one sample type of the abnormal monitoring sample to be generated; Controlling the large model to obtain a first initial monitoring sample set that meets the sample type; The large model is controlled to concentrate the first initial monitoring samples on samples that meet the prompt information and determine them as the abnormal monitoring samples.

5. The method according to claim 4, characterized in that Controlling the large model to collect samples that meet the prompt information from the first initial monitoring samples and determine them as the abnormal monitoring samples, including: Controlling the large model to perform matching processing on samples in the first initial monitoring sample set and the prompt information; The samples in the first initial monitoring sample set that successfully match the prompt information are determined as the abnormal monitoring samples.

6. The method according to claim 4, characterized in that The method further comprises: Determining a first sample quantity of the abnormal monitoring samples to be generated; Controlling the large model to determine the samples that meet the prompt information in the first initial monitoring sample set as the abnormal monitoring samples, including: controlling the large model to determine a second initial monitoring sample set that meets the prompt information in the first initial monitoring sample set, and in the second initial monitoring sample set, determining the samples whose sample number is the first sample number as the abnormal monitoring samples.

7. The method according to claim 1, characterized in that Marking the abnormal monitoring sample to generate an enhanced sample includes: Using the large model to label the abnormal monitoring sample, to obtain a first label of the abnormal monitoring sample; The enhanced sample is generated based on the abnormal monitoring sample associated with the first label.

8. The method according to claim 7, characterized in that The method further comprises: Controlling the large model to generate normal monitoring samples that do not match the prompt information; Using the large model to label the normal monitoring sample, to obtain a second label of the normal monitoring sample; The enhanced sample is generated based on the abnormal monitoring sample associated with the first label, including: determining the abnormal monitoring sample associated with the first label and the normal monitoring sample associated with the second label as the enhanced sample, wherein the enhanced sample also includes an enhanced sample that matches the normal monitoring sample.

9. The method according to claim 8, characterized in that The method further comprises: Using the abnormal monitoring samples and the normal monitoring samples as training sets for the model to be processed; The model to be processed is trained as a target model using samples of the training set.

10. A method for processing a sample, characterized in that: include: Responding to an inquiry message received in the dialogue interface, wherein the inquiry message is used to request enhancement processing of a current sample set, and the current sample set is used to train a model to be processed; In the dialogue interface, displaying the current sample set; Analyzing the current sample set using a large model, and displaying generated prompt information on the dialogue interface, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; At least displaying an abnormal monitoring sample matching the prompt information on the dialogue interface, wherein the abnormal monitoring sample is generated based on the large model; On the dialogue interface, an enhanced sample generated by marking at least the abnormal monitoring sample is displayed, wherein the enhanced sample at least includes an enhanced sample that matches the abnormal monitoring sample; Based on the enhanced sample, reply information corresponding to the inquiry information is generated and displayed on the dialogue interface.

11. The method according to claim 10, characterized in that The inquiry information is multimodal information, and the type of the multimodal information includes at least one of the following: text information containing character information, video frame information containing frame image information, and audio information. The type of the reply information includes at least one of the following: text information, image information, video information, and voice information.

12. The method according to claim 10, characterized in that The method further comprises: In response to a modification operation performed on the interactive interface, the prompt information is modified.

13. The method according to claim 10, characterized in that The method further comprises: In response to a sample update operation performed on the interactive interface, the current sample set is updated using the abnormal monitoring sample.

14. A method for processing a sample, characterized in that: include: Determine the model to be processed; Using the large model to analyze the current sample set of the model to be processed, and generating prompt information, wherein the current sample set is used to train the model to be processed, and the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; at least controlling the large model to generate an abnormal monitoring sample matching the prompt information, wherein the abnormal monitoring sample satisfies the rule; At least marking the abnormal monitoring sample to generate an enhanced sample, wherein the enhanced sample at least includes an enhanced sample that matches the abnormal monitoring sample; The enhanced samples are used as samples of a sample set, and the trained model to be processed is trained to obtain a target model.

15. A sample processing system, characterized in that: include: A data identification terminal, used for identifying a current sample set of a model to be processed, wherein the current sample set is used for training the model to be processed; A data enhancement end, used to analyze the current sample set using a large model to obtain prompt information, wherein the prompt information is used to characterize the rules satisfied by the samples marked as abnormal in the current sample set; At least controlling the large model to generate an abnormal monitoring sample matching the prompt information, wherein the abnormal monitoring sample satisfies the rule; at least marking the abnormal monitoring sample to generate an enhanced sample, wherein the enhanced sample at least includes an enhanced sample matching the abnormal monitoring sample; The model training end is used to use at least the enhanced sample as a sample of a sample set to train the trained model to be processed to obtain a target model.

16. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method described in any one of claims 1 to 14 are implemented.

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