US 12,411,876 B2
Answer information generation method
Zhuangzhuang Cui, Beijing (CN); and Bo Fu, Beijing (CN)
Assigned to BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD., Beijing (CN)
Filed by BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD., Beijing (CN)
Filed on Jun. 18, 2024, as Appl. No. 18/747,415.
Claims priority of application No. 202311596336.4 (CN), filed on Nov. 27, 2023.
Prior Publication US 2024/0411790 A1, Dec. 12, 2024
Int. Cl. G06F 16/33 (2025.01); G06F 16/3329 (2025.01); G06F 16/334 (2025.01); G06F 16/353 (2025.01); G06F 40/186 (2020.01); G06F 40/30 (2020.01)
CPC G06F 16/3329 (2019.01) [G06F 16/3347 (2019.01); G06F 16/353 (2019.01); G06F 40/186 (2020.01); G06F 40/30 (2020.01)] 17 Claims
OG exemplary drawing
 
1. An answer information generation method based on a large language model, comprising:
obtaining, in response to receiving a question text from a user, a semantic vector of the question text and event information related to a specific field, wherein the event information includes an event category concerning the question text and at least one piece of argument information in the question text;
obtaining a plurality of candidate documents from a document library of the specific field based on at least two of the semantic vector of the question text, the at least one piece of argument information and the event category;
determining, based on the event category, at least one document evaluation dimension corresponding to the event category, wherein:
the at least one document evaluation dimension corresponds to at least one aspect of document quality,
each document evaluation dimension of the at least one document evaluation dimension corresponds to a plurality of document categories, and
the plurality of document categories are determined based on an aspect of document quality that a corresponding document evaluation dimension focuses on;
determining, based on the event category, a quality score corresponding to each document category of the plurality of document categories of each document evaluation dimension;
determining quality evaluation information for each candidate document in the plurality of candidate documents based on a document category corresponding to each of the at least one document evaluation dimension of the candidate document and a quality score corresponding to the event category for the document category;
and
determining at least one target document from the plurality of candidate documents based on the quality evaluation information of each candidate document and a correlation between each candidate document and the question text, to obtain, based on the at least one target document, answer information used to answer the question text.