68 results about "Categorical models" patented technology

The invention relates to the technical field of language translation, in particular to a translation precision optimization method and system based on artificial intelligence, and the method comprises the steps: obtaining a source language text, carrying out the phrasing operation on the source language text to obtain a source language sentence sequence of the source text, carrying out the word segmentation operation on each source language sentence in the source language sentence sequence, and carrying out the word segmentation operation on each source language sentence; obtaining a word sequence of the source language sentence; and performing sentiment classification on each source language sentence in the source language sentence sequence based on a pre-trained first sentiment classification model. The sentiment classification is performed on the source language sentences and the target language sentences on the basis of the first sentiment classification model and the second sentiment classification model, and the similarity between the sentences is calculated, so that the sentences after translation are kept consistent in sentiment, the problem of sentiment deviation generated in a machine translation process is favorably avoided, and the translation efficiency is improved. Especially for some texts with strong emotion colors, it is ensured that translated emotion information can be correctly conveyed.

The invention provides a classification model training method, device and equipment based on a large language model, and relates to the technical field of natural language processing and reinforcement learning. The method comprises the steps of performing classification prediction on a first training set through a base large language model to generate an initial prediction category, and performing fine adjustment on the base large language model according to the initial prediction category and a corresponding real category to obtain a fine-adjusted large language model; through the fine-tuned large language model, screening difficult sample texts which are wrongly classified from the first training set, and constructing a second training set based on the difficult sample texts; and based on the second training set, a reinforcement learning strategy is adopted to update strategy parameters in the fine-tuned large language model, and a target classification model is obtained.

The invention discloses a Chinese text classification method based on metaphor association and label constraint comparative learning, and belongs to the technical field of natural language processing and artificial intelligence. A classification model construction method comprises the steps that an ambiguity recognition module is constructed to quantify voice and syntactic ambiguity of characters, and high ambiguity expression is recognized; a cue word management module is constructed, metaphor interpretation and label definition description are generated through a large language model, and the text semantic understanding depth is enhanced; designing a context attention mechanism and a metaphor attention mechanism, dynamically adjusting a feature weight and realizing label definition alignment; and constructing a loss function based on triple contrast learning, enhancing relevance between metaphor content and tags, and inhibiting irrelevant literal features at the same time. The Chinese text classification method is remarkably superior to a traditional model method in tasks such as metaphor sentiment classification and poetry theme classification, and the accuracy and robustness of Chinese text classification are effectively improved.

Various embodiments of the present disclosure provide prompt engineering and iterative, feedback-based generative techniques that improve traditional LLM technology, including extractive LLM techniques. The techniques may include generating, using a machine learning classification model, a resolution capability classification for an input data object that comprises an input question and an input document; generating using a large language model (LLM), an initial predictive output for the input data object based on an initial generative model prompt for the input data object; identifying a classification model output divergence based on a comparison between the resolution capability classification and the initial predictive output; and in response to the classification model output divergence: generating an augmented generative model prompt by modifying the initial generative model prompt with a representation of the resolution capability classification, and generating, using the LLM, an updated predictive output based on the augmented generative model prompt.

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.

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 an investor emotion index verification method based on machine learning, and particularly relates to the technical field of emotion index verification. Multi-source stock related texts from social platforms, financial news, brokerage reports and the like are collected, vectorization processing is performed by using a semantic representation model, and emotion recognition accuracy is improved by combining a multi-level fusion label generation mechanism and a weak supervision classification model with an attention mechanism. Dividing the annotation data into a training set, a verification set and a test set to construct a steady emotion classification model; and finally, a daily investor emotion index is constructed based on model output, the effectiveness of the daily investor emotion index is verified through the correlation with market variables and the predictive ability, a label system and model parameters are iteratively optimized based on a verification result, a sustainable emotional analysis closed loop is formed, and the emotion recognition precision and the explanatory power of the index are remarkably improved through the method.

The invention provides a knowledge distillation method and device based on multi-loss function combination and TOP-K. The method comprises the steps that training texts are input into a teacher model and a student model respectively to execute a text translation task, and teacher probability distribution and student probability distribution are obtained; k highest probability values in the teacher probability distribution and corresponding categories are stored, and probabilities corresponding to other categories are zeroed to obtain TOP-K probability distribution; constructing a plurality of loss functions based on the difference between the student probability distribution and the TOP-K probability distribution, and training the student model; inputting the training text into the trained student model to obtain a performance index of the student model, judging whether to continue to train the student model or not according to the performance index, if yes, executing the initial step again, and if not, storing the current student model as a translation model, and inputting to-be-translated text data into the classification model to obtain a translation result.

The invention discloses a big language model illusion detection method and system, and relates to the technical field of natural language processing and artificial intelligence. Comprising the steps that 1, a question and answer data set is selected, twenty internal state vectors generated when a large language model generates answers to an input question are extracted, and the internal state vectors are semantic coding vectors of a hidden layer in the model reasoning process; 2, the twenty internal state vectors are constructed into a sample matrix, and a covariance matrix of the sample matrix is calculated; 3, calculating a standardized determinant of the covariance matrix, wherein the standardized determinant is a twentieth-power root of a determinant value of the covariance matrix; 4, taking the obtained standardized determinant value as a feature F1, and taking the token number of the answer output by the large language model as a feature F2; 5, training a support vector machine dichotomy model by using the question and answer data set, wherein labels of training samples are illusion or non-illusion; and step 6, inputting F1 and F2 into the trained support vector machine dichotomy model, and outputting a hallucination or non-hallucination detection result.

Embodiments of the disclosed technology include a representation learning model for classification of natural language text. In embodiments, a classification model comprises a feature model and a classifier. The feature model may be hierarchical in nature: data may pass through a series of representations, decreasing in specificity and increasing in generality. Intermediate levels of representation may then be used as automatically learned features to train a statistical classifier. Specifically, the feature model may be based on a hierarchical Pitman-Yor process. In embodiments, once the feature model has been expressed as a Bayesian Belief Network and some aspect of the feature model has been selected for prediction, the feature model may be attached to the classifier. In embodiments, after training, potentially using a mix of labeled and unlabeled data, the classification model can be used to classify documents such as call transcripts based on topics of conversation represented in the transcripts.

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.

The invention discloses a financial supervision and inspection problem text classification method and device, equipment and a storage medium. The method comprises the following steps: acquiring a financial supervision inspection question text; inputting the financial examination question text into the target classification model to obtain a risk result output by the target classification model; wherein the target classification model is obtained by pre-training on the basis of a BERT model, and a lexicon table of the BERT model is obtained by expanding a lexicon table corresponding to a SentencePiece word segmentation device. According to the scheme provided by the embodiment of the invention, on the basis of the Bert model, the word library table of the BERT model is expanded by introducing SentecePiece word segmentation, that is, the target classification model is obtained through easy-to-difficult training, so that the model fully learns more feature information in a financial supervision inspection problem text, and the purpose of improving the classification accuracy of the model is achieved.

A method, apparatus, and computer-readable medium are described that identify subject matter of text using identified groups and a machine-learning model. Using the combination of the identified subject matter and the machine-learning model, classifications may be adjusted over time. Based on the adjusted classifications, the machine-learning model may be retrained to better classify previously unclassified text. One or more benefits may include better summarization of text documents and / or better training of machine-learning models that are then used to assist in the summarization of the text documents. The resulting classifications may be used to improve resource allocations for future tasks.

The invention provides an interpretable text classification method based on large model concept generation, and relates to the field of natural language processing. In the training stage, firstly, stable and interpretable concept features and corresponding dimensions are extracted and marked for each sample through a large model, so that a task-aware concept system is constructed; and then coding each labeled sample to carry out prediction modeling to obtain a text classification model with high interpretability. In the reasoning stage, firstly, experience samples are screened for new samples on the basis of a screening strategy combining dual semantic consistency and sample diversity enhancement; and then marking concepts of new samples and dimensions corresponding to the concepts through a large model by utilizing a task-aware concept system and an experience sample, and then feeding back a marking result to a text classification model to finish final prediction. According to the method, the processing efficiency and the prediction accuracy of the text classification task are improved, the transparency, the stability and the business controllability of classification model output are enhanced, and application scene requirements with relatively high interpretability requirements are met.

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.

The present invention discloses a Chinese text classification method based on metaphor association and label-constrained contrastive learning, which belongs to the field of natural language processing and artificial intelligence technology. The classification model construction method includes: constructing an ambiguity recognition module to quantify the phonetic and syntactic ambiguity of characters and identify highly ambiguous expressions; constructing a prompt word management module to generate metaphor interpretations and label definition descriptions using a large language model to enhance the depth of text semantic understanding; designing a dual mechanism of contextual attention and metaphorical attention to dynamically adjust feature weights and achieve label definition alignment; constructing a loss function based on triple contrastive learning to enhance the correlation between metaphorical content and labels while suppressing irrelevant literal features. The present invention significantly outperforms traditional model methods in tasks such as metaphor emotion classification and poetry theme classification, effectively improving the accuracy and robustness of Chinese text classification.

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 invention provides a method, a classification method, a device and a device for establishing an insurance user classification model. The method includes: obtaining the user insurance data and user attributes of historical insurance users, and the regional insurance data of the regions to which the historical insurance users belong; determining the type characteristics of different data in the user insurance data and the regional insurance data; performing binning encoding processing on the continuous data in the user insurance data and the regional insurance data to obtain the converted continuous data, and integrating the categorical data, the encoded continuous data in the user insurance data and the regional insurance data, and the user attributes into training set data; using the training set data to train a preset initial classification model to obtain a trained insurance user classification model. Through the insurance user classification model, the accuracy of classifying existing insurance users is improved, and thus reliable guidance is provided for insurance services.

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.

A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. A data appended procedure supplements historical applications data with public records and credit risks. Various features of this data are engineered for improved predictive characteristics. Fluidless models are trained by application of a random forest ensemble including survival, regression and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer. If any of the fluidless predictive models determines a high risk target, the applicant is required to submit clinical data.

Embodiments relate to methods and systems for media classification that can be used to better improve classification models and understand the outcome of such classifications. Various embodiments may determine a classification for a media item, and may determine a cue that includes the classification for a media item. Additional embodiments may train a machine learning model using media items with known classifications. The results of each training iteration may be used to understand the results and improve the machine learning model.

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.

A computerized method includes: obtaining a first dataset of pre-labeled textual items, wherein each pre-labeled textual item is associated with a pre-label; feeding each of the pre-labeled textual items into a Large Language Model (LLM), and prompting it to generate textual reasoning that supports the pre-label of each pre-labeled textual item; collating the generated textual reasonings, and generating therefrom a textual instruction prompt; obtaining a second dataset of not-yet- labeled textual items; feeding each of the not-yet-labeled textual items into the LLM, and commanding it to utilize the textual instruction prompt and to generate a textual label for each of the not-yet-labeled textual items; collecting those textual items, that were labeled by the LLM, into a third dataset of LLM-labeled textual items; automatically training a Machine Language (ML) classification model on that third dataset of LLM-labeled textual items; deploying that ML classification model in a platform for classification of textual items.

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.

The present invention proposes a method for training a federated learning classification model based on model perturbation, and the implementation steps are as follows: constructing a federated learning classification system; the client initializes the local classification model and training parameters of the federated learning; the client performs iterative training on the local classification model and perturbs the local classification model; the central server obtains the training results of the federated learning system. According to the different types of features of the training data set extracted by each convolutional layer and the different influences of each fully connected layer on the model prediction classification effect, the client of the present invention assigns different privacy budget values to different model layers of the local classification model, which can control the perturbation degree of each model layer, avoid the influence of the perturbation degree of the model layer being too large or too small on the model, and thus improve the privacy protection ability and prediction classification accuracy of the federated learning classification model.

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.

The invention discloses a learning effect classification research method fusing BERT model and SAGEonv graph convolution. The method comprises the steps of data collection and preprocessing; collecting various kinds of process evaluation data of students in a certain period of time, and arranging the data into a format suitable for model input; constructing and training a model; the pre-trained BERT model and the SAGEEConv graph convolutional network are fused, and a classification model LABTG is constructed; performing hyper-parameter optimization; the performance of the model is optimized by adjusting hyper-parameters of the model, so that the optimal classification prediction effect is achieved; evaluating and applying the model; and using an independent test set to evaluate the classification prediction performance of the model, and according to teaching evaluation data of different stages, assisting a teacher to estimate the learning effect category of the student at the end of the stage. According to the invention, the process evaluation data of students can be analyzed more comprehensively, and the accuracy of learning effect classification prediction is improved; and real-time teaching effect feedback is provided for teachers, so that accurate teaching is realized.

The present invention discloses a method for updating a teacher model based on accuracy and related devices, mines the model performance (accuracy) as a judgment condition, constructs a more flexible method for updating the teacher model, and further improves the performance of the student model in the self-distillation scenario. The present invention incorporates the performance of the teacher model (such as the accuracy of the classification model) into the decision-making process of the teacher model, and uses the calculated minimum performance difference as the condition for replacing the teacher model, taking into account both the stability and performance of the teacher model, and further improving the learning effect of the student model.