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1.发明公开公开(公告)号：US20240303430A1
公开(公告)日：2024-09-12
申请号：US18667504
申请日：2024-05-17
Inventor： Meng TIAN , Lin YANG , Xinwei FENG , Zhifan FENG , Xiaopeng CUI , Qiaoqiao SHE , Hua WU
IPC: G06F40/20
CPC classification number: G06F40/20
Abstract: A technical solution for processing a model generation result, which relates to the field of artificial intelligence technologies is disclosed. An implementation includes: disassembling a text generation result of a generative large model to obtain a plurality of result logic units; wherein each result logic unit includes a segment in the text generation result; each segment is capable of independently identifying one premise or conclusion in a logical inference relationship of the text generation result; and the text generation result is a response result generated by the generative large model based on text input information; generating a logical inference graph capable of characterizing a logical inference relationship among the plurality of result logic units based on the plurality of result logic units; and determining whether logical inference of generation of the text generation result by the generative large model is correct or not based on the logical inference graph.
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2.发明公开公开(公告)号：US20240354658A1
公开(公告)日：2024-10-24
申请号：US18745529
申请日：2024-06-17
Inventor： Feng HE , Jianhua WANG , Junjie OU , Pingxuan HUANG , Zhifan FENG , Xiaopeng CUI , Qiaoqiao SHE , Hua WU
Abstract: A method and apparatus for training a question solving model, a question solving method and apparatus, an electronic device and a readable storage medium are disclosed. The method for training a question solving model includes: acquiring a first sample question; inputting the first sample question and a solving step grabbing template into a large language model to obtain a first sample solving step; inputting the first sample question, the first sample solving step and an answer grabbing template into the large language model to obtain a first sample answer; pre-training a step planning model according to the first sample question and the first sample solving step; pre-training the large language model according to the first sample question, the first sample solving step and the first sample answer; and acquiring the question solving model according to the step planning model and the large language model obtained by pre-training. The question solving method includes: acquiring a to-be-solved question; inputting the to-be-solved question into a step planning model to obtain a solving step; and inputting the to-be-solved question and the solving step into a large language model to obtain an answer.
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