8 results about "Basic language" patented technology

The invention discloses an English listening and speaking interaction intelligent training system based on a large language model, and relates to the technical field of English learning, and the training system comprises a user capability graph module, an interaction feedback module, a personalized learning path generation module, a scene simulation module, an interaction rhythm control module and a community cooperative training module. According to the method, the pragmatic ability, the cross-language communication ability and the linguistic ability are jointly incorporated into the training content, and the adaptive training content is generated in combination with a real cross-language communication scene, so that a user synchronously masters expression specifications and culture adaptation key points in different scenes in the training process; the effect that training content and real communication requirements are deeply matched is achieved, the user is helped to accurately adapt to a native English scene and real phrase habits of people, the requirements of an actual application scene are met, and substantial improvement from basic language knowledge mastering to real communication ability is achieved.

The invention discloses a large language model training method, device and equipment applied to a professional field and a storage medium, and the method comprises the steps: obtaining a related document of a current field, and carrying out the chapter splitting of the related document, and obtaining a plurality of text blocks; generating a plurality of structured question and answer pairs based on each text block, wherein each structured question and answer pair comprises questions and answers corresponding to the document chapters to which each text block belongs; and inputting each text block and each structured question and answer pair into a basic language model, and performing iterative training on the basic language model according to a preset progressive fine tuning strategy to obtain a target large language model. The domain document can be subdivided according to chapters and sections, structured question and answer training is performed on each text block, and then the model is trained by simulating a human learning process through the preset progressive fine tuning strategy, so that terminologies and concepts can be integrated step by step and iteratively; therefore, the knowledge adaptability of the model in the specific field is remarkably improved.

The application provides a language model reasoning method and device based on hierarchical state memory bank and combined initialization, a terminal, a medium and a product. The method comprises the following steps: constructing a hierarchical state memory bank; obtaining user query information, and performing hierarchical retrieval in the hierarchical state memory bank based on the user query information to obtain a best state set; performing combined initialization on the best state set to generate an initial state; and enabling a basic language model to start reasoning on the user query information in the initial state to generate a corresponding reasoning result. Through hierarchical retrieval, the application can provide matched context for queries of different granularities, solve the cold start problem of the existing linear attention language model in the form of RNN, and make the reasoning result of the model more accurate.

The invention relates to a code internationalization processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: constructing an abstract syntax tree of a to-be-processed file, and extracting a to-be-translated character string set from the to-be-processed file based on the abstract syntax tree; semantic translation keys are generated, the character strings to be translated are associated with the corresponding semantic translation keys, and a key value pair set to be translated is formed; replacing the semantic translation keys in the translation key value pair set with general internationalization function calling through a target parser to obtain internationalization transformation codes; storing the to-be-translated key value pair set into a basic language package; performing multi-language translation on the basic language package, and respectively storing translation contents after the multi-language translation into a plurality of corresponding language packages; and when code internationalization processing needs to be carried out, reading translation content in a target language package corresponding to the target language through the internationalization transformation code. By adopting the method, contents which do not need to be translated can be effectively filtered.

This invention relates to the field of artificial intelligence application technology, specifically to a power knowledge question-answering system and method based on a large language model. The system includes a question-answering knowledge base for constructing a power knowledge graph and a dual index; a query rewriting module that parses the user's original query and outputs a standardized rewritten query; a knowledge retrieval module with dual recall and ranking that outputs Top-K results; and a content generation module that generates answers and performs closed-loop optimization after reflection and evaluation. The method includes constructing a power knowledge graph and dual index, rewriting the original query based on a large language model, obtaining Top-K results through dual recall and ranking, and reflectively generating and optimizing answers. This addresses the problems of weak semantic understanding in existing power knowledge question-answering technologies, insufficient professionalism and reasoning ability of the basic language model, and lack of a systematic knowledge organization structure.

The invention provides a few-sample intention detection method, medium, equipment and product, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the pre-training of a basic language model through employing an offline learning mode, and obtaining a prediction model; the prediction model receives user query data online in an online learning mode, a candidate intention list with sorting is generated, and whether the first intention in the candidate intention list meets a preset confidence threshold value or not is judged; if yes, outputting the first intention; if not, forming a reduced candidate set by the first K candidate intentions in the candidate intention list, and obtaining a large model prediction intention by using a large language model; if the large model prediction intention is consistent with the first intention, outputting the large model prediction intention; if not, the large language model outputs a final intention according to the user query data, the first intention, the large model prediction intention and comparative context information of example query. According to the invention, accurate few-sample intention detection is realized.

The invention discloses a large language model reasoning acceleration method based on speculative decoding, and relates to the technical field of natural language processing, and the method comprises the following steps: S100, freezing a basic language model; s200, extracting a hidden state of the key layer; s300, constructing a de-wharf network set; s400, training a de-wharf network; and S500, deploying the basic language model. According to the method, a multi-solution wharf network structure is adopted, and a parallel draft and verification mechanism is combined, so that the reasoning efficiency of the basic language model is improved, the resource consumption is reduced, and meanwhile, the consistency and the accuracy of a generated result are considered.

The application discloses an e-commerce customer service reply method, system, device and medium. The method comprises the following steps: presetting a basic language model, a first language model and a second language model; inputting a first historical customer service reply context into the basic language model to obtain first data output by the basic language model; performing distillation learning based on the first language model according to the basic language model, and performing low-rank adaptive fine-tuning by using the first data to obtain a third language model; inputting a second historical customer service reply context into the third language model to obtain second data output by the third language model; performing low-rank adaptive fine-tuning by using the second data based on the second language model to obtain a target language model; and inputting a reply request into the third language model and the target language model to obtain reply content corresponding to the reply request. The three-level architecture of the model and the mixed inference training strategy can be combined to improve the reply accuracy, logic and interpretability.