60 results about "Word target" patented technology

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 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.

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.

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 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.

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 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.

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.

The application discloses a kind of classroom teaching auxiliary systems for classical Chinese knowledge explanation, comprising: text acquisition module, obtains the full-text of target argument type classical Chinese and course knowledge point mapping table;Text preprocessing module, full-text is segmented and segmented to text;Text marking module is used to mark target word from full-text, and the sentence belonging is marked as target sentence;Text feature coding module generates the feature vector of target word and the context concatenation feature vector of target sentence;Text analysis module, the feature vector of target word and the context concatenation feature vector of target sentence are matched with course knowledge point mapping table respectively to output the explanation and usage of target word and the explanation of target sentence;Text information output module, output the explanation and usage of target word and the explanation of target sentence.

The invention relates to the technical field of natural language processing, and discloses a synonym replacement-based text data enhancement method, which comprises the following steps of: retrieving sentences containing vocabularies from a corpus and sampling; performing word segmentation and coding on the sentences obtained by sampling, and extracting context representation vectors of vocabularies by using a pre-training language model; clustering the context representation vectors of the same vocabulary in different contexts to obtain a central semantic representation vector; storing the vocabularies and the corresponding central semantic representation vectors into a vector database; for a target vocabulary in the sentence to be processed, searching similar candidate vocabularies in the vector database by using the corresponding context representation vector; performing integral gradient calculation to obtain candidate word scores; and sorting the candidate words according to the candidate word scores to generate an enhanced sentence. According to the method, the polysemy vector representation of the vocabularies is stored by utilizing the vector database, the candidate vocabularies are sorted in combination with the integral gradient, and diversified training data are generated, so that the model training efficiency and the data utilization effect are improved.

The present disclosure provides a question and answer method, device, equipment and storage medium, which can be applied to the field of artificial intelligence and finance. The method comprises: determining a question text vector; determining N target word vectors; determining answer information according to the N target word vectors; wherein, the N target word vectors are determined by repeatedly performing the following operations until the N target word vectors are obtained: under the condition that 1 < n ≤ N, determining an n th first candidate word vector set from a preset word vector set according to the question text vector and the first n-1 target word vectors in the preset word vector set; randomly dividing the n th first candidate word vector set to obtain a plurality of n th first candidate word vector subsets; determining an n th second candidate word vector set from the n th first candidate word vector subsets according to the n th first candidate word vector set, the question text vector and the first n-1 target word vectors in the preset word vector set; and determining an n th target word vector according to the n th second candidate word vector set.

The embodiment of the invention provides a model training method, a word vector generation method and device, equipment and a medium, and is applied to the technical field of computers. The model training method comprises the following steps: acquiring a plurality of sample vocabularies; obtaining a grayscale image of each sample vocabulary; for the grayscale image of each sample vocabulary, determining a vocabulary vector of the sample vocabulary according to the grayscale value of each pixel point in the grayscale image; and performing model training according to each sample vocabulary and the vocabulary vector of each sample vocabulary, and generating a vocabulary vector generation model for predicting the vocabulary vectors corresponding to adjacent vocabularies. When word vectors are generated on the basis of a word vector generation model, the word vector generation method comprises the following steps: acquiring words to be processed; and inputting the to-be-processed vocabulary into the vocabulary vector generation model, and obtaining a target vocabulary vector output by the vocabulary vector generation model. Based on the model training method and the use of the vocabulary vector model, the effect of improving the word vector learning efficiency is achieved.

The invention belongs to the technical field of artificial intelligence, and relates to an artificial intelligence-based data processing method, which comprises the following steps of: processing a target image and a description text based on a large visual language model to generate lexical elements; if a modal prejudice quantification result obtained by quantifying the lexical elements meets attention intervention conditions, calculating an original attention matrix of the lexical elements, and performing weight adjustment on the original attention matrix based on the modal prejudice quantification result to obtain a specified attention matrix; calculating the original attention matrix to obtain first conditional probability distribution, and calculating the specified attention matrix to obtain second conditional probability distribution; fusing the first conditional probability distribution and the second conditional probability distribution to obtain target conditional probability distribution; and decoding the target conditional probability distribution to generate a target lexical element, and generating and outputting a target text based on the target lexical element. The method can be applied to image analysis scenes in the financial science and technology field and the medical field, and the accuracy of the generated target text is improved.

This invention proposes an intelligent enterprise accounting voucher generation and compliance review system, comprising: a text processing module, including a target lexicon generation unit, which can extract several negative words, several modifier texts paired with each negative word, and several correction words from preprocessed enterprise accounting text data and generate a target lexicon; and a current text recognition module, which is used to calculate the part-of-speech relevance C of the current text based on the target lexicon, and to identify the current text as a negative word or a corrected word based on the part-of-speech relevance C and the context of the current text, so as to perform a correction operation based on the recognition result, thereby solving the problem that the existing system has insufficient ability to analyze the scope of negative words and the consistency of context before and after correction, which leads to the existing system easily misclassifying negative statements and ignoring the associated impact of correction operations, thus causing voucher generation errors or compliance review loopholes.

The invention provides a position coding method, device and equipment for a structured sequence. The method comprises the following steps: acquiring a target lexical element in a structured input sequence of a target language model and position information of the target lexical element in the structured input sequence; obtaining a multi-dimensional structure coordinate of the target lexical element according to the position information of the target lexical element in the structured input sequence; according to the multi-dimensional structure coordinates, rotating each structure dimension of the target lexical element to obtain a multi-dimensional rotating field of the target lexical element; and obtaining a position coding result of the target lexical element in the target language model according to the multi-dimensional rotation field of the target lexical element. According to the scheme, the multi-dimensional structure information can be fused to perform position coding on the input sequence, and the processing performance of the structured task of the target language model is improved.

The embodiment of the invention provides a method, device and equipment for generating reply information. According to the scheme, the method comprises the steps that key vectors and value vectors of all lexical elements in lexical element sequences corresponding to all text blocks are calculated in advance in an off-line mode; in the online stage, target lexical elements with high semantic importance in target text blocks are recognized by adopting a lexical element screening model aiming at the target text blocks which are recalled through retrieval and contained in cue words, and then on one hand, key vectors and value vectors of the target lexical elements can be calculated online, and on the other hand, the key vectors and the value vectors of the target lexical elements can be calculated online; on the other hand, obtaining a pre-calculation key vector and a pre-calculation value vector of a non-target lexical element generated offline; and the large language model can generate reply information aiming at the user query information based on the key vector and the value vector of the target lexical element generated online and the pre-calculation key vector and the pre-calculation value vector of the non-target lexical element generated offline.

The invention discloses a task processing method, system and device for multi-model fusion and electronic equipment, and relates to the field of large models. The method comprises the following steps: receiving a task description text; respectively calling the first model and the second model to iteratively process the task description text, and in each round of iteration, according to a first output result and a first mapping matrix of the first model and a second output result and a second mapping matrix of the second model, determining a target lexical element corresponding to the round of iteration in a union set word list of the first model and the second model; and after iteration is finished, a task execution text corresponding to the task description text is determined according to the target lexical element corresponding to each round of iteration, and a task execution result obtained after task execution is recorded in the task execution text. According to the method and the device, the technical problem that the fusion model cannot be conveniently used for processing the task due to the fact that the cost of fusing the model is too high in the related technology is solved.

The invention provides a target business processing method and device, equipment, a medium and a product, relates to the technical field of big data, and can improve the accuracy and efficiency of correlation business of a subject object in a target field. According to the specific scheme, the method comprises the following steps: acquiring associated record information of a plurality of main objects in a target field, a target field parameter system and a target word library corresponding to the target field; and performing entity extraction on each piece of associated record information to obtain a plurality of associated entities corresponding to each main body object in the target domain. According to the similarity matching result between the associated entity and the target keyword, the to-be-processed object is screened from the main body objects, all the main body objects do not need to be processed, and the processing efficiency is improved. Through step-by-step processing from the low-level parameter values to the high-level parameter values, the obtained quantitative processing result fully reflects multi-dimensional and multi-level processing parameters of the parameter system, and the accuracy of the quantitative processing result is improved.

The invention discloses a word sequence determination method and device in semantic recognition, a storage medium and an electronic device, and relates to the technical field of smart home, and the method comprises the steps: carrying out the linear projection of an implicit vector sequence corresponding to a voice signal, so as to obtain a multi-granularity posterior probability of the voice signal, the multi-granularity posterior probability comprises a character posterior probability, a sub-word posterior probability and a word posterior probability; searching a word set and a character set associated with the sub-words in the voice signal in a multi-level mapping table, and calculating a target path score of the sub-words in the voice signal according to the multi-granularity posterior probability, the word set and the character set; and inputting the target path score into a target decoder to obtain a target sub-word sequence, and obtaining a target word sequence corresponding to the target sub-word sequence according to the multi-level mapping table. According to the method and the device, the problem of homophone misrecognition of'pronunciation synonym error 'of rare characters, polyphone characters, technical terms and the like in an end-to-end ASR process in the related technology can be solved.

This specification provides one or more embodiments of a large-scale model retrieval enhancement generation method and apparatus. First, a target index sequence obtained based on privacy-preserving retrieval is acquired, and the corresponding target dataset is determined from a local knowledge base and merged into multiple data sample subsets. Then, a word-by-word generation loop is executed. In each loop, a first-layer utility function is constructed based on the conditional probability distribution output by the text output model, and a first differential privacy perturbation is performed to obtain candidate words. Then, the relative probability advantage of the candidate words is determined, and a second-layer utility function is constructed, and a second differential privacy perturbation is performed to obtain target words. Simultaneously, the target words are appended to the context fragment to complete the update. After the termination condition is met, the target words determined in each loop are concatenated to obtain desensitized synthetic data, which is then sent to the data receiver for generating response results of the user query text through a large language model.

Embodiments of the present application provide a text processing method and device, equipment and storage medium, relating to the technical field of artificial intelligence and natural language processing. The method comprises: obtaining a target sentence text, and performing word segmentation processing on the target sentence text to obtain a target word group; inputting the target word group into a small network model to obtain a first word group embedding representation; inputting the first word group embedding representation into a nonlinear mapping model to obtain a second word group embedding representation; and obtaining a sentence embedding representation according to the second word group embedding representation, wherein the accuracy of the output result of the nonlinear mapping model can approach the output result of a pre-trained large network model, thereby making up for the problem of inaccurate output result of the small network model. Since the output result of the small network model is fitted to the output result of the large network model by using the simple structure of the nonlinear mapping model, the processing time will not increase significantly, and the balance between accuracy and timeliness is achieved.

The embodiment of the invention provides a text processing method and related equipment, and the method comprises the steps: obtaining M candidate words, and obtaining a candidate text set corresponding to each candidate word, the candidate text set comprising N candidate texts containing the corresponding candidate words; m is a positive integer, and N is an integer greater than 1; performing semantic space mapping processing on the candidate words in the candidate texts to obtain word vectors of the candidate words in the corresponding candidate texts; according to the word vector of each candidate word in the corresponding candidate text, determining the semantic difference of each candidate word between any two corresponding candidate texts; and according to the semantic difference of each candidate word between any two corresponding candidate texts, determining a target word from the M candidate words, the target word being a candidate word having a plurality of semantics in a corresponding candidate text set. According to the embodiment of the invention, polysemy words can be efficiently determined.