4 results about "Topic sentence" patented technology

The invention relates to a construction method and device of an evaluation data set of a large language model, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining dialogue state theme statements corresponding to different evaluation dimensions; expanding the dialogue state theme statement to obtain an expanded dialogue state theme statement; optimizing the expanded dialogue state theme statement to obtain a to-be-analyzed statement; the richness of the dialogue state information of the to-be-analyzed statement is higher than the richness of the dialogue state information of the expanded dialogue state theme statement; taking the to-be-analyzed statement and the corresponding dialogue state analysis result set as an evaluation data set; the dialogue state analysis result set comprises a correct analysis result and at least one analysis result different from the correct analysis result; the evaluation data set is used for evaluating the dialogue state perception ability of the pre-trained large language model. By adopting the method, the dialogue state perception capability of the large language model can be evaluated more accurately.

The application provides an off-topic detection method and system for compositions. The method extracts key features of questions from topic sentence encoding by a neural network model, and extracts key features of answers from composition encoding. The key features of questions and answers are input into an attention mechanism layer, ensuring that the features input into the attention mechanism layer are key features, reducing interference caused by other useless features when the attention mechanism layer processes data, and improving the accuracy of data processing by the attention mechanism layer. On this basis, the attention mechanism layer determines the features of the topic sentence that the composition is concerned about, and the features of the composition that the topic sentence is concerned about, completing feature selection from two directions, ensuring the comprehensiveness of the selected features, enabling the neural network model to classify based on more comprehensive features, improving the accuracy of the classification result, and thereby improving the accuracy of the detection.

The invention provides a theme extraction method and device for group discussion, electronic equipment and a storage medium. The method comprises the following steps: acquiring a time sequence text sequence in a group discussion process; based on the text segmentation model, dividing discussion record data in the time sequence text sequence to obtain a plurality of subject areas; and based on a large language model, performing subject extraction on the discussion record data in each subject area, and determining a discussion subject of each subject area. Through the text segmentation model constructed by the PoNet, the BiLSTM, the Dropout layer and the full connection layer, the discussion record data in the time sequence text sequence is divided to obtain a plurality of subject regions, so that the text structure is clearer. The semantic comprehension and analysis capability of the large language model is utilized to automatically extract and generate the topic sentence for each topic paragraph as the representation of the topic, so that automatic text processing and topic analysis are realized, and the efficiency and accuracy of discussing topic determination are improved.

The application relates to an artificial intelligence technology and discloses a multi-layer attention-based dialogue generation method which can be used in the medical or financial fields, comprising the following steps: performing word segmentation on dialogue text to obtain a word segmentation sequence set; using a first encoder to encode the word segmentation sequence set to obtain a topic sentence vector set; using an attention mechanism to weight the topic sentence vector set to obtain a final sentence vector set; using a second encoder to perform fusion encoding on the final sentence vector set, calculating the similarity between a fusion vector and the topic sentence vector set, weighting and summing the topic sentence vector set according to the similarity to obtain an overall topic vector; using a self-attention mechanism to weight the final sentence vector set to obtain an attention vector set; and using a decoder to decode and splice the overall topic vector and the attention vector set to obtain an answer text. The application further discloses a multi-layer attention-based dialogue generation device, an electronic device and a storage medium. The application can improve the dialogue generation accuracy.