108 results about "Word target" patented technology

The invention relates to the technical field of artificial intelligence, finance and medical health, and provides a retrieval method, device and equipment based on attention guidance and a medium. Training data is constructed according to the correlation between each specified lexical element and a retrieval question and a retrieval answer and the attention feature vector of each specified lexical element; a logistic regression classifier is trained to obtain a detector, and the light-weight detector can be used for recognizing utilized lexical elements in real time in the reasoning process, so that the higher-degree explanation of the generation process is realized; when the retrieval model is used for retrieval, the detector is used for detecting the target lexical elements which are being used in real time, the original probability distribution is corrected according to the utilization probability of each target lexical element so as to control the retrieval model to output the retrieval result, and the output probability of the used lexical elements is dynamically enhanced. Therefore, the attention mechanism of the guide model is dynamically focused on the context information highly related to the user query, and the context illusion phenomenon is fundamentally reduced.

The invention provides a text reinforcement learning method and device, electronic equipment and a computer storage medium, relates to the technical field of text reinforcement learning, and is applied to a text generation model. The method comprises the following steps: determining one or more target vocabularies in a statement to be analyzed, and generating a replacement word set according to the target vocabularies; replacing the target vocabulary in the to-be-analyzed statement with a current candidate replacement word to obtain a replacement statement; calculating one or more difference index values between the replacement statement and the to-be-analyzed statement; wherein the difference index value is used for quantifying the influence of the replacement of the target vocabulary on the original sentence from different dimensions; based on the difference index value, determining an importance weight of the target vocabulary in the to-be-analyzed statement; wherein the importance weight is used for redistributing the reward of the single character in the sentence. Vocabulary importance is quantified through disturbance analysis and multi-dimensional evaluation, and then fine-grained distribution of rewards is achieved.

According to the method and device for generating the fine-grained semantic description from the action video data, the training data set is established based on the isolated word sign language recognition data set and the continuous sign language recognition data set containing the word target notation, and the action video data and the action description text data of fine-grained semantic description modeling are obtained; a fine-grained semantic action description style pre-training generation model is obtained through a training framework comprising an action video feature coding module, a multi-modal feature fusion module and a text feature coding module by combining user cue words and system cue words and introducing a mask reconstruction mechanism, action video data is adopted for fine tuning, a loss function is established, and a fine-grained semantic action description style is obtained. The fine-grained semantic action description generation model is used for generating high-quality fine-grained semantic action description data, and the problem that the current fine-grained semantic action description data is insufficient is solved. And the stability and the accuracy of a generated result are ensured when high-dynamic complex scenes such as sign language videos and interactive actions are processed.

The invention discloses a speculation decoding method and device based on a large language model, equipment and a medium, and relates to the technical field of large models, and the method comprises the steps: determining the output probability distribution of a plurality of lexical element positions, and determining a confidence score based on the output probability distribution; determining a target control parameter according to the target state signal, performing aggregation calculation on the confidence coefficient vector to obtain an aggregation confidence coefficient, and determining a target draft length through the aggregation confidence coefficient and the target control parameter; determining a verification tolerance threshold based on the target control parameter and the confidence score, generating a draft sequence based on the target draft length by using a target deep learning model, and verifying each lexical element in the draft sequence based on a preset large language model and the verification tolerance threshold; and adding the successfully verified target lexical units to the end of the currently generated sequence to obtain a target sequence, determining the target sequence as the currently generated sequence, and skipping again to perform the next round of speculative decoding. According to the invention, the efficiency of speculative decoding can be improved.

The embodiment of the invention provides an observation information generation method, device and equipment. According to the scheme, the method comprises the steps that in the process that a generative large language model generates reply information for a target request, a probability distribution vector of a target lexical element is collected; then, based on the probability distribution vector, determining recommendation lexical element information with high probability; acquiring sampling parameter information corresponding to the target request; afterwards, the recommended lexical element information, the sampling parameter information and the target lexical element are stored in an associated mode, and quality observation information is obtained so as to be used for analyzing reasons for quality abnormity of the reply information.

The invention discloses a text data classification method and device, electronic equipment and a nonvolatile storage medium. The method comprises the steps of obtaining target text data; adopting a first model to match the target text data with vocabularies in a target word bank, and determining a first score corresponding to the target text data according to a matching result, a second model is adopted, the first semantic feature corresponding to the target text data is matched with a target semantic feature library, a second score corresponding to the target text data is determined according to the matching result, and the operand and complexity of the second model are larger than those of the first model; and determining a classification result of the target text data according to the first score and the second score. According to the method and the device, the technical problem of poor text data classification effect caused by limitation of a single model on semantic comprehension and classification decision in related technologies is solved.

The invention relates to the technical field of artificial intelligence, is suitable for the financial field and the medical health field, and provides a verbal skill generation method and device, equipment and a storage medium, and the method comprises the steps: obtaining a user input text of which a topic deviates from a business topic, and extracting a target word group from the user input text; determining a first response template text from a plurality of preset response template texts associated with different business themes; generating a second response template text based on the target word group and the first response template text; analyzing the user input text and the second response template by using a preset language model to generate a plurality of target lexical elements for responding to the user input text, and obtaining probability data corresponding to each target lexical element; and based on the multiple target lexical elements and the probability data corresponding to each target lexical element, generating a target response verbal skill used for guiding the topic to return to the business theme. The method can help an artificial intelligence customer service to naturally guide the dialogue to return to the business theme.

This application relates to the field of artificial intelligence security technology and discloses a method, apparatus, device, and medium for security optimization of a large language model, comprising: acquiring an attack task dataset; wherein the attack task dataset includes at least one attack task type and multiple malicious instruction data under the attack task type; generating a general word-level adversarial suffix corresponding to the attack task type based on the malicious instruction data, and generating a target word-level adversarial suffix corresponding to the malicious instruction data based on the general word-level adversarial suffix; performing a first fine-tuning on the large language model based on the target word-level adversarial suffix to obtain a first fine-tuned model; and performing a second fine-tuning on the first fine-tuned model based on the acquired target semantic-level adversarial hints to obtain a second fine-tuned model. This application can improve the ability of a large language model to resist various known and unknown forms of jailbreak attacks.

This application discloses a vocabulary prediction method, apparatus, storage medium, and computer device, wherein the method comprises: obtaining a target text for a target user, obtaining each first target vocabulary in the target text and the order relationship between each first target vocabulary; using a grade evaluation model to obtain a target text grade of the target text based on each first target vocabulary and the order relationship between each first target vocabulary; using a vocabulary prediction model to obtain a text vocabulary corresponding to the target text based on each first target vocabulary and the vocabulary grade corresponding to each first target vocabulary; and determining a predicted vocabulary of the target user based on the target grade vocabulary grade and the text vocabulary. Using this application, the accuracy of vocabulary prediction is improved.

This application provides a text processing method, apparatus, device, and storage medium. The method involves segmenting the text to obtain several words to be grouped; calculating a first semantic similarity between the words to be grouped; grouping the words to be grouped into word combinations based on the first semantic similarity, with each word combination containing words to be grouped with similar semantics; searching for words in the word combinations that do not match a preset correct word as replacement words; replacing the replacement words with the preset correct word to obtain a target word combination; and using the target word combination to correct the word combinations in the text to obtain the target text. This application, based on the first semantic similarity, automatically performs consistency checks and processing on the text in the front-end system, improving the efficiency and accuracy of the front-end system's text consistency checks, thereby saving manpower and time costs.

Embodiments of the present application provide a cross-model collaborative reasoning method and device, equipment and a storage medium, relating to the technical field of intelligent computing equipment. The input prompt is sent to the first model for reasoning to generate a probability value corresponding to each word element at the current time step. The highest probability value is determined from the probability value, and the marginal benefit of the current time step is determined according to the highest probability value. The current benefit threshold corresponding to the current time step is obtained, and the decision variable is determined according to the marginal benefit and the current benefit threshold. The target word of the current time step is obtained based on the decision variable, and the output sequence corresponding to the input prompt is obtained according to the target word sequence before the current time step and the target word. The on-demand switching of the double model is realized by the dynamic determination of the marginal benefit and the benefit threshold. The first model is used to realize low-energy-consumption reasoning at the time step with high reasoning confidence. The second model is used to ensure accuracy at the key time step with low reasoning confidence. The reasoning accuracy is ensured while reducing the reasoning energy consumption.

The application discloses a semantic recognition model based on NPL and a training method thereof, relates to the technical field of semantic recognition model training, and aims to solve the problem of low recognition accuracy of a semantic recognition model. The current whole sentence is searched, multiple segmented sentences are screened out, words in the segmented sentences are recognized, the part-of-speech information of the words is collected, the part-of-speech weight under the corresponding part-of-speech information is called, the number of repeated occurrences of each word in the current whole sentence is counted and the repeated word frequency is calculated, the processing level of each word is calculated by comprehensively considering the repeated word frequency and the part-of-speech weight, the target words are screened out and recognized, the semantics of the target words are acquired and the number of semantics is counted under the current interactive window, the interactive duration of the target words is analyzed by calling historical data, the number of word reference sources is set as a reference, the number of semantics of the target words and the interactive duration are comprehensively considered to adjust the number of word reference sources of each target word, and the semantic analysis accuracy of each target word in the interactive sentence is improved.

Disclosed are a word vector generation method and device. The word vector generation method includes: obtaining corpus data including at least two text units, each text unit including at least one word; determining the weight of each text unit in the corpus data based on the distribution of each group word in a group word set and each target word in a target word set in the text units of the corpus data; determining sample data for training a word vector model based on the corpus data and the weight of each text unit in the corpus data; training the word vector model using the sample data, and obtaining the word vector of at least one word in at least one text unit in the corpus data from the trained word vector model.

The application discloses a cross-media model error attribution and auxiliary correction method based on causal decoupling, which comprises the following steps: image segmentation is performed on an input image, and the input image is divided into a plurality of sub-regions; a target function is solved according to the sub-regions, the minimum key region which supports the generation of a target word element to the greatest extent and removes the generated ability is sorted according to the contribution to the generation decision, and an attribution saliency map is generated; an influence score is calculated according to the change of the generation probability of the target word element in the process of inserting the sub-regions in the ordered subset; the ordered subset, the attribution saliency map and the influence score are evaluated from the dimensions of fidelity, positioning ability and error correction guiding ability, and an evaluation result is obtained; the evaluation result is obtained without relying on the internal gradient, attention weight or activation map of the model, the dependence on the internal structure of the model is reduced, the regional attribution of an arbitrary word element set is realized, and the relative dependence of the generation process on visual evidence and language prior is quantified.

Embodiments of the present specification disclose a key information extraction and live content processing method, device and equipment. The scheme comprises: obtaining original text to be extracted key information; determining an attention weight vector for the original text, and correspondingly generating a first target word according to the attention weight vector; judging whether the first target word is included in the original text; if yes, obtaining the attention weight of the first target word when generating the first target word according to the attention weight vector, generating penalty data for the first target word according to the attention weight of the first target word, and continuing to generate a second target word according to the penalty data, wherein the penalty data is used to reduce the possibility that the second target word generated continues to be the same as the first target word; and generating key information according to the first target word and the second target word.

The invention provides a code generation method and device, electronic equipment and a storage medium. The method comprises the following steps: analyzing at least one piece of key information in demand information corresponding to a natural language search text; in order to determine the importance degree of the key information, respectively generating second probability distribution obtained according to semantic features of the natural language search text and first probability distribution corresponding to a preset lexical element library obtained according to mask semantic features, since the mask semantic feature is the semantic feature corresponding to the natural language search text after hiding the key information, the influence degree of the key information on the probability distribution of the generated lexical elements can be determined according to the difference distribution between the first probability distribution and the second probability distribution. According to the method, the difference value distribution and the second probability distribution are obtained, the target lexical element sequence is determined according to the difference value distribution and the second probability distribution, the accuracy of the target lexical element sequence is improved, finally, the target code is generated according to the target lexical element sequence, and the matching degree of the target code and the demand information corresponding to the natural language search text is improved.

Systems or techniques that facilitate systems and methods for providing explainability of natural language processing are provided. In various embodiments, a system can access a plain text clinical sentence. In various aspects, the system can generate, via execution of a first machine learning model, an assertion status classification label for a word of interest in the plain text clinical sentence. In various instances, the system can extract, from a hidden attention layer of the first machine learning model, word-wise attention scores corresponding to the plain text clinical sentence and render, on an electronic display, both the assertion status classification label and a graphical representation of the word-wise attention scores. In various cases, the system can determine, via execution of a second machine learning model, a reliability score for the assertion status classification label, based on the word-wise attention scores, and can render the reliability score on the electronic display.

The invention discloses a semantic sentiment analysis method, an intelligent cabin, a medium and a vehicle. According to the scheme, user voice is split, and global semantic information is put into a token sequence for coding; a target lexical element semantic code of each independent neural network is screened from the target lexical element semantic codes by using a gating network, and is processed into an emotion semantic feature; newly adding a target neural network, and generating shared semantic features in combination with the gating network; therefore, shared semantic features, global semantic codes and own emotion semantic features are fused in each independent neural network, when a specific emotion category is analyzed, local emotion semantic features are focused to serve as a detail basis, and overall emotion deviation can be grasped by means of the global semantic codes, so that the emotion classification accuracy is improved. In addition, semantic support common with all emotion categories can be obtained by sharing semantic features, and deep mining is performed on local and overall association, so that judgment of each emotion category can give consideration to detail expression and global emotion deviation, and the emotion analysis ability of the intelligent cockpit is improved.

The invention discloses a text causal relationship extraction method and device, electronic equipment and a storage medium. The method comprises the steps of determining a first text; determining at least one group of target causal relationship phrases in the first text based on a pre-generated word causal relationship table; and according to the at least one group of target causal relationship phrases and the target influence parameters corresponding to the target causal relationship phrases, generating at least one target word causal graph. By the adoption of the technical scheme, the unrelated or non-keyword can be effectively deleted to reduce the workload of the process of determining the target first word and the target second word later, so that the determining efficiency of the target first word and the target second word is improved, and the efficiency of determining the target first word and the target second word is improved through a greedy-based fast causal reasoning algorithm. According to the embodiment of the invention, the causal relationship of each candidate word vector is determined, and the text undirected graph is generated, so that the causal relationship of each candidate word vector can be exposed firstly, and the analysis efficiency of causal analysis can be improved during subsequent causal analysis.

The application discloses a kind of fusion knowledge enhancement and the method and system of original meaning prediction of confrontation training, it is related to natural language processing technical field, obtain target word and its corresponding original dictionary explanation;Original dictionary explanation is carried out semantic integrity evaluation using semantic perception selective enhancement intelligent agent;Target word, original dictionary explanation and supplementary context are structured as structured input sequence;Structured input sequence is sent into original meaning encoder, semantic features are extracted, and original meaning prediction vector is obtained;Using the method based on fast gradient, dynamic confrontation disturbance is applied to word embedding layer during model training process, clean sample loss and confrontation sample loss are optimized jointly to update model parameters;The original meaning label set corresponding to target word is output.The application introduces two big mechanisms of selective knowledge enhancement and confrontation training, and systematically solves the problem that decision boundary is weak due to static dictionary information bottleneck and original meaning long tail distribution in traditional original meaning prediction.

The application provides an intention recognition method and device, electronic equipment and storage medium. The intention recognition method comprises: performing word segmentation processing on a target sentence in current session text from a customer to obtain at least one target word; determining whether there is a word with the same character as a plurality of keywords in a preset keyword table in the at least one target word, and if so, determining the word with the same character as a candidate word; calculating the similarity value between at least part of the keywords in the preset keyword table and the candidate word to obtain at least one similarity value; when there is a similarity value greater than or equal to a first preset threshold in the at least one similarity value, determining the context information of the target sentence; and performing intention recognition on the target sentence according to the context information to obtain a target intention label. The technical scheme of the application can realize the real-time of intention recognition, and improve the accuracy of the recognized intention label.

Embodiments of the present specification relate to a text desensitization method based on differential privacy, a program product and a computing device. The method comprises: obtaining a cluster set, any target word in any cluster has a preset replacement probability distribution, the replacement probability distribution is determined based on an exponential mechanism, and is used to describe the probability of replacing the target word with each word in the cluster; performing named entity recognition on the input text to be desensitized to determine the entity type to which each word belongs, which includes an entity type preset as a sensitive word type; for any target sensitive word belonging to the sensitive word type, it is judged whether it exists in any cluster of the cluster set, when the target sensitive word exists in a first target cluster, a target replacement word is obtained by sampling according to the replacement probability distribution of the target sensitive word in the first target cluster; and the target sensitive word in the text to be desensitized is replaced with the target replacement word to obtain a desensitized text.

An embodiment of the present invention discloses a cross-lingual fine-tuning method, device, equipment, and storage medium. The method includes: obtaining translation sample pair data, where the translation sample pair data includes a first source text corresponding to a source language and a first target text corresponding to a target language; performing target near-synonym replacement on some source words in the first source text to determine a second source text after replacement, where the target near-synonym refers to a target word with a semantic similar to the source word; performing source near-synonym replacement on some target words in the first target text to determine a second target text after replacement, where the source near-synonym refers to a source word with a semantic similar to the target word; based on the first source text, the second source text, the first target text, and the second target text, performing cross-lingual fine-tuning on a pre-trained model to determine a pre-fine-tuned model. Through the technical solution of the embodiment of the present invention, the cross-lingual difference between the upstream self-supervised task and the downstream cross-lingual task can be reduced, thereby improving the fine-tuning effect.

The invention discloses a task processing method and device based on a task knowledge base, equipment and a storage medium, and relates to the technical field of semantic communication, and the method comprises the steps: converting an index sequence of a target information source semantic representation vector into a first lexical element sequence of a large language model based on an information source semantic representation knowledge base; on the basis of a channel semantic representation knowledge base, converting the channel state information into a second lexical element sequence of the large language model; splicing the structured task demand prompt information, the first lexical element sequence and the second lexical element sequence into target prompt information, and inputting the target prompt information into a large language model for reasoning to obtain a target lexical element sequence; and based on the property of the target task, processing the target lexical element sequence to obtain a task processing result. Through the mode, a bidirectional conversion mechanism between the multi-modal task / channel environment semantics and the lexical elements of the large language model is constructed, so that the large language model can understand and process diversified task data from a dynamic channel.

The present application provides a training method and device for a question-and-answer model. The training method for the question-and-answer model includes: obtaining training samples and extracting target word units in the training samples; constructing sample word groups corresponding to the training samples according to the target word units; querying a pre-generated scene-oriented word list space based on the sample word groups, and determining a target text semantic group corresponding to the training samples according to the query result; using the target text semantic group and the training samples to train an initial question-and-answer model until a target question-and-answer model that meets the training stop condition is obtained.

The present application discloses a linear structure-based information extraction method, device, and medium. The method includes: designing a linear structure encoding, jointly representing entity information and entity relationships, setting a prompt template, training a pre-trained language model to obtain a generative language model, using an attention residual structure in the training of the pre-trained language model, obtaining natural language data, encoding the corresponding input sequence based on the linear structure encoding, encoding it through an encoder, and decoding it through a decoder according to the input order of the input sequence, obtaining a first prediction probability of a target vocabulary output by the decoder, obtaining a second prediction probability of the target information, and obtaining the target information based on the second prediction probability. The method realizes the joint extraction of entity information and relationship information, effectively integrates entity information and relationship information, and makes the two information extraction tasks have the same underlying operation in the training of the model, thereby enhancing the dependency between tasks.

The present application discloses a method and device for dispatching work orders, a processor and an electronic device, and relates to the field of artificial intelligence. The method includes: obtaining a first potential feature of multiple target words in a target work order, wherein the target work order is a work order to be dispatched, and the target words are words other than stop words in the target work order; combining the fully connected layer and the normalized exponential function of the neural network, based on the first potential feature, a plurality of probability values ​​are calculated, wherein the probability value is the probability value of the target work order corresponding to each organizational level; according to the plurality of probability values ​​and the first preset threshold, the target organizational level corresponding to the target work order is determined; based on the target organizational level, the target organization corresponding to the target work order is determined, and the target work order is dispatched to the target organization. Through the present application, the problem of low accuracy in dispatching work orders in the related art is solved.

The invention discloses a model reasoning control method and device, a medium and a product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a probability distribution sequence of lexical elements in a pre-training language model reasoning process, the probability distribution sequence comprises current probability distribution of the lexical elements in the current reasoning step and historical probability distribution of the lexical elements in the historical reasoning step; extracting current probability distribution and historical probability distribution, identifying a current ranking of a preset target lexical element in a current step and a historical ranking of the preset target lexical element in a historical step, and generating a ranking sequence according to the current ranking and the historical ranking; according to the method and the device, the ranking sequence is ranked, an abortion reasoning step of the pre-training language model is positioned according to the ranking sequence and a preset reasoning abortion threshold value, and a reasoning process of the pre-training language model is controlled based on the abortion reasoning step, so that the technical problems of excessive thinking and calculation waste caused by incapability of accurately identifying a convergence critical point in related technologies are solved; the technical effects of reducing redundant thinking and reducing calculation overhead are achieved.

The invention discloses a text vocabulary recommendation method, system and device and a medium, and the recommendation method comprises the steps: obtaining a first text input content of a user and a vocabulary semantic map of the user; performing text analysis on the first text input content to obtain a target keyword, text context information and text emotion information; according to the vocabulary semantic map, performing context matching and knowledge query processing on the target keyword and the text context information to obtain context expansion information corresponding to the first text input content; and inputting the text emotion information, the context expansion information and the first text input content into a pre-training language model for text generation to obtain a target vocabulary, and providing the target vocabulary to the user. According to the method, the individuation degree and accuracy of text vocabulary generation can be effectively improved, and the learning efficiency and learning experience of the user can be improved. The invention relates to the technical field of natural language processing.

This invention relates to the field of natural language processing technology, providing a word recommendation method, apparatus, electronic device, and storage medium. The method first obtains the definition to be queried; then, based on a reverse dictionary model, selects target words corresponding to the definition from a candidate word list; finally, based on the target words, determines the recommendation result. This method utilizes a reverse dictionary model obtained through multi-task learning to predict words and their parts of speech, considering the parts of speech rather than solely relying on the quality of definitions in the dictionary and the quality of the user-input definition. This ensures the accuracy and quality of the recommendation results, thereby improving the user experience. The introduction of parts of speech helps reduce the prediction space, constrains the recommendation results, makes the results more reliable, reduces interference from easily confused words, and avoids situations where the recommended results differ significantly from the user-input definition.