23 results about "Categorical models" patented technology

The invention provides a sentiment classification method based on a visual language model and conditional reasoning, which comprises the following steps of: firstly, acquiring a text picture pair and labeling sentiment labels on the text picture pair to form a sentiment label set; then, a visual language model is used as a strategy model, general reasoning and conditional reasoning are carried out on the text picture pairs respectively, reasoning characterization, emotion prediction labels and conditional reasoning results are generated, and response samples are formed after the reasoning characterization, the emotion prediction labels and the conditional reasoning results are combined; and calculating a reward value and an advantage estimation value based on the response sample to optimize the strategy model, finally utilizing the optimized strategy model to carry out sentiment prediction on a new text picture pair, and outputting a final sentiment classification result. According to the method, the defect that a traditional multi-classification model is easily interfered by noise texts or complex visual contents is overcome, the classification precision is improved, a general reasoning process and a conditional reasoning process of a strategy model are recombined into a group of response samples, it is guaranteed that each group of response contains different classification labels, and the problem of advantage collapse is solved.

The invention discloses a psychological state text classification method based on large model generative data enhancement, and relates to the technical field of natural language processing. According to the method, patient psychological semantic clusters are automatically found by clustering a limited number of original samples, and then a natural language semantic template of each patient psychological semantic cluster is extracted; then, under the dual control of emotional polarity and psychological themes by utilizing a large language model, according to the natural language semantic templates, psychological state texts with consistent themes and diversified expressions are generated as enhanced samples; according to the method, high-quality sample generation is carried out by utilizing a natural language semantic template obtained based on clustering through a large language model so as to expand a model training sample, and the authenticity and diversity of the generated enhanced sample can be ensured through the large model generation type data enhancement method; therefore, the classification accuracy and generalization ability of the psychological state text classification model obtained through training are improved.

Techniques for detecting machine learning model drift are described. Model drift can result in the model misclassifying inputs. A system for detecting drift in natural language processing (NLP) models involves determining high-dimensional embeddings of inputs and high-dimensional embeddings of training samples, reducing the high-dimensional embeddings to low-dimensional embeddings, and comparing the low-dimensional embeddings to determine whether the inputs are statistically different than the training samples. When the inputs are statistically different than the training samples, model drift is detected, and retraining of the model may be performed. The system can detect drift in other classification models as well and can process with respect to other types of inputs (e.g., audio, image, etc.).

Systems, methods, and apparatuses are disclosed for detection and prevention of adversarial attacks against large language models. Techniques may include receiving an input associated with a target large language model, analyzing the input with a pre-trained classification algorithm to determine a first deconstruction process to be applied to the input, and modifying the input with a first deconstruction model using the determined first deconstruction process. Techniques may also include determining a score of a likelihood of the input being adversarial based on an output of the first deconstruction model and by applying a classification model and updating at least one of the first deconstruction model or the classification model based on the score.

A system and method include training a classification model to classify data based on first data associated with a first usage scenario, receiving second data associated with a second usage scenario inputting the second data to the classification model and receiving a likelihood of a first classification from the classification model, determining a similarity between the second data and a plurality of data associated with the second usage scenario, modifying the likelihood based on the determined similarity, determining a second classification of the second data based on the modified likelihood, and processing the second data according to the second classification of the second data.

The present application relates to the field of artificial intelligence, in particular to a case retrieval method based on multiple models. Various multi-source data are collected and integrated; the semantic dependency relationship between the text fragments of the contradiction is captured by using Bert, and the similarity of the case data is calculated by using the BM25 algorithm; a model is trained by Bert according to twelve types of classifiers, and at the same time, a model is retrained for the overall similarity model training. The similarity model constructed by combining the case classification model in the prior art and the present application completes the accurate case retrieval of diversified and long-length contradiction dispute data. The legal case data of the present application is more specific and perfect, contains sufficient legal knowledge, can cope with the change of legal rules, the unification of diversified legal data, accurate collection, efficient auxiliary analysis of legal cases and resolution work. The present application is more accurate in calculating the similarity, and the relevant category of legal cases is recommended accurately.

The invention belongs to the field of urban calculation and intelligent transportation, and discloses a hierarchical fine-grained human trajectory activity type inference method and related devices.The method comprises the steps that firstly, rigid activity types are recognized through predefined anchor point rules, and efficient labeling of regular segments is ensured; subsequently, the remaining staying segments mark transactional or leisure motivations through a motivation classification model to differentiate internal heterogeneity of non-rigid activities; and finally, performing structured inference on the marked fragment by a specific large language model of the motivator, and outputting a fine-grained non-rigid activity type. By the adoption of the method, the overall accuracy and robustness of fine-grained activity inference are remarkably improved, the performance is optimized especially on identification of non-rigid activities, adaptability to complex multi-constraint scenes is enhanced, and reliable fine-grained classification requirements are supported.

The invention discloses a psychological assessment method and system based on dialogue content, and relates to the technical field of semantic processing, and the method comprises the steps: obtaining question and answer dialogue data of a user and a system in a multi-round interaction process; encoding each round of question sentences and answer sentences by using a semantic analysis model, and extracting semantic embedding vectors and topic feature vectors; calculating a semantic matching degree between adjacent question and answer pairs, and marking the semantic matching degree as a semantic deviation sample when the matching degree continuously decreases; constructing a topic embedding sequence of multiple rounds of dialogues and counting a fuzzy word proportion to form a topic and a statistical feature vector; inputting the semantic deviation samples, the themes and the statistical features into a machine learning classification model to obtain a psychological situation classifier; and outputting an evaluation result during dialogue operation. The problem that psychological reasons for topic avoidance of users in multiple rounds of dialogues are difficult to quantify in real time is solved.

Methods, systems, devices, and non-transitory computer readable media for training machine-learning models are provided. The disclosed technology can include receiving input samples associated with classification concepts. Based on inputting the input samples into a first plurality of machine-learned models, classification outputs comprising labels and confidence scores can be generated. The first plurality of machine-learned models can comprise one or more multimodal large language models (LLMs) and one or more domain-specific models. Annotated input samples comprising the input samples, the classification outputs, and identifiers that identify each of the first plurality of machine-learned models that generated each of the classification outputs can be generated. Furthermore, based on the annotated input samples, one or more second machine-learned models can be trained. The training can comprise modifying parameters of the one or more second machine-learned models based on the confidence scores.

This invention belongs to the field of brain network technology and relates to a method and system for constructing a classification model based on standardized brain network modeling. The construction method includes: acquiring electroencephalogram (EEG) signal data of a subject; determining the partial orientation coherence adjacency matrix corresponding to the EEG signal data; processing the partial orientation coherence adjacency matrix according to a first formula to determine the individualized causal directed brain network partial orientation coherence matrix corresponding to the EEG signal data to be tested, wherein the feature path length and network density in the individualized causal directed brain network partial orientation coherence matrix are in an optimal balance state; predicting the individualized directed brain network partial orientation coherence matrix to be tested using a standardized benchmark model, and determining the deviation between the predicted value of the standardized benchmark model and the true value of the individualized directed brain network partial orientation coherence matrix; the standardized benchmark model is a model established based on the brain network adjacency matrix of healthy subjects; and constructing a classification model using the deviation as a classification feature.

The invention discloses a large language model training method and device and a target classification model generating method and device. The method comprises the steps of determining a target training task and at least one historical training task to be subjected to memory training; obtaining a first synthetic instance set corresponding to the target training task; obtaining a second synthetic instance set corresponding to the historical training tasks in the at least one historical training task, and determining a target synthetic instance set according to the first synthetic instance set and the second synthetic instance set; and training a to-be-trained large language model according to the target synthesis instance set to obtain a target large language model. The technical problem that old knowledge of the model is maintained depending on real training data of a large language model in related technologies, and new and old knowledge of the large language model cannot be considered when the real training data is unavailable is solved.

The invention discloses an emotion classification and model training method and device, a medium, a product and equipment, and the method comprises the steps: independently converting obtained text information into first prompt information corresponding to a generation model and second prompt information corresponding to a classification model, the first prompt information is input into a first model containing a generative model to obtain a first feature, so that the first feature can contain rich semantic and context information learned by the generative model, and the second prompt information is input into a second model containing a classification model to obtain a second feature; according to the method, the first feature and the second feature are combined, so that the second feature can contain the preliminary classification information learned by the classification model, and the third feature with richer information content can be obtained through the first feature and the second feature, so that the richness of the feature information received by the prediction model is improved, and the accuracy of an emotion prediction result output by the prediction model is improved.

This application discloses a method for classifying psychological state texts based on large-scale generative data augmentation, relating to the field of natural language processing technology. This method automatically discovers patient psychological semantic clusters by clustering a limited number of original samples, then extracts the natural language semantic template for each patient's psychological semantic cluster. Subsequently, under the dual control of emotional polarity and psychological theme, a large-scale language model is used to generate psychological state texts with consistent themes and diverse expressions according to these natural language semantic templates as augmented samples. This method utilizes the large-scale language model to generate high-quality samples based on the natural language semantic templates obtained from clustering to expand the model's training samples. This large-scale generative data augmentation method can ensure the authenticity and diversity of the generated augmented samples, thereby improving the classification accuracy and generalization ability of the trained psychological state text classification model.

The invention relates to the technical field of resource management, and provides a supply chain risk quantification method based on large language model weak supervised learning. The method comprises the following steps: obtaining a multi-time-period operation disclosure text of a target enterprise and cleaning clauses to form a structured statement set; screening a potential risk statement subset based on the supply chain risk keyword library; calling a large language model to carry out weak supervision labeling to generate a risk category label; training a small risk sentiment classification model by using a label, reasoning a full amount of statements, and outputting a probability value of each risk category; aggregating the probability according to a time period to obtain a semantic distribution vector to represent a risk semantic state; and constructing a semantic residual image for the adjacent periodic vector difference values, and calculating a supply chain risk trend index according to the semantic residual image to realize risk evolution trend quantification.

The invention provides an electric power system semantic framework analysis method and system based on a pre-trained language model, and the method comprises the steps: recognizing a sentence in an electric power system text through a pre-trained framework recognition model, and obtaining a target word in the sentence and a framework type activated by the target word; performing sequence labeling on the sentence by utilizing a pre-trained argument recognition model to obtain an argument range of the target word; utilizing a pre-trained argument role classification model to predict and classify arguments in the sentences based on the frame type and the argument range to obtain semantic role tags of the arguments; determining a power system event contained in the sentence based on a frame type and a semantic role tag corresponding to the argument; according to the method, the key steps of the semantic framework analysis task are subjected to task association, so that the whole semantic analysis process is more accurate and efficient, the finally obtained event analysis result of the electric power system is more accurate, and the decision of the electric power system and the accuracy of related instructions are ensured.

This application relates to a method, system, device, and medium for predicting ethical risks in multi-agent collaborative systems. The method includes: acquiring real-time interaction logs of multiple agents in the education field, obtaining a dynamic change sequence of interactions through time-series analysis; dividing time stages based on interaction frequencies exceeding a preset threshold, calculating a stage risk evolution index, and quantifying the mean value by combining it with core ethical risk indicators to generate an educational ethical risk early warning vector; based on this vector, training a Bayesian network using Bayesian estimation to obtain a joint probability distribution of risk evolution; generating risk evolution paths from the distribution using the Monte Carlo method, and identifying propagation path types using a machine learning classification model; inputting this type of time-series data into a pre-trained long short-term memory network, and weightedly fusing it with real-time interaction data to generate educational ethical risk prediction parameters. This method can improve the timeliness and accuracy of risk early warning, avoid related ethical risks, and contribute to the standardized and safe development of artificial intelligence education.

The invention discloses a bidding life cycle and announcement type identification method adopting text classification, and the method comprises the steps: obtaining bidding announcement information from an open channel, analyzing an original bidding announcement, carrying out the data preprocessing, extracting key information according to preset rules and labels, balancing the label distribution of sample data, and carrying out the fine adjustment based on ERNIE. According to the method, life cycle and type identification of bidding announcements is performed by combining a rule method and deep learning, categories of multi-source public bidding announcement information are summarized in a unified manner, information integration is facilitated, classification and identification are performed based on the deep learning model, and the classification efficiency is improved. According to the method, complexity and maintenance difficulty of a rule-based method are avoided, only a small part of data is manually labeled for sample data of model training, labor cost is saved, and the accuracy and efficiency of identifying bidding life cycles and announcement types are improved.

The application discloses a kind of psychological assessment method and system based on dialogue content, it is related to semantic processing technical field, including obtaining the question and answer dialogue data of user and system in the process of multiple rounds of interaction;Utilize semantic analysis model to encode each round question and answer, extract semantic embedding vector and theme feature vector;The semantic matching degree between adjacent question and answer is calculated, and is marked as semantic deviation sample when matching degree continuously falls;The theme embedding sequence of multiple rounds of conversation is constructed and the proportion of fuzzy words is counted, to form theme and statistical feature vector;Semantics deviate sample and theme and statistical features are input into machine learning classification model, and obtain psychological situation classifier;Output evaluation result when conversation runs.The application solves the problem that the psychological reason of user to avoid topic in multiple rounds of conversation is difficult to be quantified in real time.

The invention discloses a construction method for large language model content output compliance evaluation, which comprises the following steps: S1, enabling a compliance evaluation agent to generate an evaluation result by combining an original output text of a tested large language model according to a compliance judgment standard; the answer refusing evaluation agent presets an answer refusing or non-answer refusing standard and enables the agent to output a result and a judgment reason in combination with an original output text; and S2, extracting structured annotation data as an input sequence, constructing a Longform dichotomy model, configuring sliding window attention for each annotation in the input sequence by adopting a sparse attention mechanism, and combining the sliding window attention with global attention configured for specific classification annotations. According to the method, the compliance evaluation agent, the answer rejection evaluation agent and the Longform dichotomy model are constructed for double-path evaluation, the compliance and answer rejection results are combined with the dichotomy result and the interpretation statement of the large model for joint output, and the reliability and the interpretability of the evaluation result are improved.

The invention relates to the field of network security and artificial intelligence, and discloses a technique and tactics classification method based on incremental learning, which comprises the following steps: firstly, carrying out regularization preprocessing on an original threat intelligence text, and carrying out semantic enhancement by utilizing a large language model to relieve sample scarcity and category imbalance; then, a classification model capable of being dynamically expanded is constructed, the model comprises a technique and tactics encoder, a label attention mechanism and a lightweight classifier, and in combination with a low-rank self-adaptive fine tuning strategy, rapid adaptation of newly-added categories is achieved; and through combined optimization of weighted classification loss and two types of knowledge distillation loss, and by adopting reservoir sampling, a playback memory library with a fixed capacity is maintained, so that disastrous forgetting in an incremental learning process is reduced. According to the method, efficient and stable automatic technology and tactical classification can be realized in a scene of continuously updating the threat tactics.

The invention relates to a dense point recognition method and device combining entropy guidance and large language model test enhancement. The method comprises the following steps: firstly, through oversampling balance training data, constructing a classification model by using a lightweight pre-training model; dividing a to-be-recognized document into abstract and sentence sequences, and inputting the abstract and sentence sequences into the quantized large language model to generate an enhanced sample; and screening effective samples according to the entropy variation, and inputting the effective samples and the original sentences into a classification model. And processing a prediction result through a preset aggregation strategy, and finally outputting the security level of each sentence. The process efficiently balances data and enhances samples, and improves security classification recognition accuracy. By adopting the method, the problems of poor generalization performance of dense point recognition, uncontrollable enhanced sample quality, context semantic loss and insufficient confidential environment adaptability in a few-sample scene can be solved.

The embodiment of the invention discloses a coal structured data automatic classification method based on a large language model and a KNN classification model, and the method comprises the steps: firstly setting a fixed topic domain according to the business of a coal industry, and then carrying out the preprocessing of collected structured data; a large language model trained by industry corpora is utilized to perform deep semantic analysis and generate labels and quantitative scores, then a KNN classification model is combined to perform automatic classification and subject domain induction according to a scoring result, the accuracy is improved through verification of a loop optimization mechanism, and finally efficient and automatic classification of data is realized. According to the method, the coal data classification efficiency and precision can be remarkably improved, the compliance and business fitting degree of classification results are enhanced, meanwhile, good adaptability and flexibility are achieved, efficient, accurate and compliant automatic classification of the coal structured data is achieved, and the coal structured data can be summarized to a fixed subject domain.