6 results about "Precision and recall" patented technology

Systems and methods for implementing auditing of large language model-based tools for bias in inferences is disclosed. Individual entries of the dataset of dialogs may be modified to include stereotypical details of particular contexts. These modified records may then be submitted to an automated response generator to produce a set benchmark records. The baseline records and benchmark records may then be analyzed for completeness, accuracy and conciseness with respect to the particular contexts and disparities in precision and recall may be determined using differences in the benchmark and baseline records. The determined disparities may then be used to further train or fine-tune the automated response generator.

This application relates to the field of data processing technology, and in particular to an AI multi-source data processing method and system based on big data. The method includes: calculating the field correlation degree based on the number of times each field co-occurs with all other fields in the field co-occurrence matrix; using the ratio of the number of non-empty records to the total number of records as the data completeness; and using the product of the field correlation degree and the data completeness as the field weight; randomly sampling content samples multiple times for each field to calculate aggregation stability, and calculating the cross-source similarity between any two fields from different data sources; constructing an adaptive similarity threshold for each field; and for each pair of cross-source fields, if the cross-source similarity is not lower than the adaptive similarity threshold, determining that the field pair is semantically matched, and outputting the field mapping relationship. The technical solution of this application can reduce the false matching rate of key fields while maintaining the overall matching recall rate, achieving synergistic optimization of matching precision and recall.

The application discloses a kind of knowledge graph construction methods based on fine-grained retrieval and reverse restoration self-error correction, specifically: first, standard reference case library is constructed, and standard reference vector is calculated.Then, the long text S to be measured is disassembled into sentences to be processed;Each sentence is vectorized, and the similarity score of its standard reference vector is calculated, and the first standard reference case of each sentence is screened;Through large language model, the non-standard logical relationship contained in each sentence is extracted and vectorized, the cosine similarity is calculated, and the candidate mapping is obtained by descending arrangement, after summarizing, double-feature reordering is carried out using multi-round greedy reordering algorithm, and the first standard reference case is screened out, and the single sentence is spliced into large language model, and triple extraction is carried out;Finally, the relationship triple set of all sentences constitutes knowledge graph.The application improves the precision and recall rate of triple extraction under the premise of avoiding redundancy.

The application relates to a corpus construction method and system, which comprises the following steps: establishing a multi-dimensional marking system according to clinical characteristics of epilepsy, and assigning different marking strategies to different types of labels; obtaining original clinical text data related to epilepsy, marking the original clinical text data by using the differential marking strategy, and forming labeled marking corpus; performing consistency evaluation on the obtained labeled marking corpus, and iteratively revising the marking rules when the consistency is lower than a preset threshold until the consistency meets the standard; using the labeled marking corpus as a supervision signal to train and verify an entity recognition model, and obtaining an entity recognition model for epilepsy clinical text; and applying the trained entity recognition model to unmarked epilepsy clinical text to generate a structured corpus. The application can solve the problems of low extraction accuracy, coarse granularity and poor adaptability of the current method, and the method can output high-quality labeled corpus with high precision and recall.

The application provides a grammar error correction large model training method and device based on reinforcement learning, equipment and medium, relating to the technical field of data processing. The method comprises the following steps: obtaining an initial grammar correction corpus containing error sentences and corresponding correct sentence pairs, processing the initial grammar correction corpus to generate a reasoning correction training set, adjusting a first preset language model according to the reasoning correction training set to obtain an initial strategy model, performing reinforcement learning training on the initial strategy model based on a preset composite function by using a reinforcement learning algorithm, and finally obtaining a target grammar correction model. The target grammar correction model can fully utilize the reasoning ability, effectively improve the performance of the model in terms of precision and recall, better meet the actual needs of grammar correction, and generate correction results containing reasoning processes, which provides more transparent and interpretable basis for the correction process.

The invention discloses a multi-modal sentiment analysis model based on multi-head attention perception fusion, and the model comprises the steps: firstly, carrying out the feature extraction of a multi-modal multi-head attention mechanism, enabling the feature to be projected to a vector subspace, reducing the dimension of the feature, keeping the most important sentiment feature information, and making preparations for the subsequent analysis; and secondly, performing data calculation on the multi-modal features by using labels of an emotion data set, and projecting the multi-modal features in a vector space to realize fusion of the multi-modal features, thereby effectively integrating different perception channel information from texts, audios and videos. And finally, carrying out experiments on the multi-modal data sets of the CMU-MOSI and the CMU-MOSEI, thereby verifying the effectiveness and the performance of the multi-head attention mechanism in the multi-modal sentiment analysis task. Experimental results show that the multi-modal sentiment analysis method introducing the multi-attention mechanism is remarkably improved in the aspects of recognition accuracy, sentiment scores, precision and recall rate.