-
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.
-
Patent Agency Ranking
公开(公告)号：US20230147798A1
公开(公告)日：2023-05-11
申请号：US18052143
申请日：2022-11-02
Inventor： Haifeng WANG , Hao TIAN , Jing LIU , Hua WU , Tian WU , Yu SUN , Qiaoqiao SHE
CPC classification number: G06F16/3347 , G06F40/30
Abstract: A method is provided. The method includes converting a search request of a user into a first request semantic vector. The method further includes searching a search resource database for at least one first data semantic vector matched with the first request semantic vector, wherein the search resource database is constructed as a semantic vector space in which different types of data are converted into corresponding data semantic vectors, and the different types of data include at least texts, pictures and videos. The method further includes generating, based on the at least one first data semantic vector, a search result.
-
3.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.
-
公开(公告)号：US20240338862A1
公开(公告)日：2024-10-10
申请号：US18749461
申请日：2024-06-20
Inventor： Jiachen LIU , Xinyan XIAO , Hua WU , Guohao LI , Wei LI , Hong ZHU , Qiaoqiao SHE , Yajuan LV
Abstract: A method is provided that includes: obtaining current dialogue data; determining a requirement type of the user in the current round of dialogue based on the current dialogue data; in response to the requirement type being an image processing requirement, determining an action sequence for implementing the image processing requirement; executing the action sequence to generate a target image; and generating response data corresponding to the user input data based on the target image.
-
公开(公告)号：US20220198153A1
公开(公告)日：2022-06-23
申请号：US17694034
申请日：2022-03-14
Inventor： Jian GONG , Yu SUN , Hao TIAN , Hua WU , Haifeng WANG , Qiaoqiao SHE
IPC: G06F40/40 , G06F40/284 , G06F40/205
Abstract: A model training method, a model training platform, an electronic device and a storage medium are provided, which can be used in the field of artificial intelligence, particularly the fields of natural language processing and deep learning. The model training method includes: receiving an input; determining, based on the input, a user-oriented prefabricated function; determining, based on the input, a model training function; determining, based on the input, a pre-trained model; determining, based on the input, a network structure associated with the pre-trained model so as to support use of the pre-trained model; training, based on the input, the model by using the prefabricated function, the model training function, and the pre-trained model; and providing an output associated with a trained model.
-
6.发明公开公开(公告)号：US20240338564A1
公开(公告)日：2024-10-10
申请号：US18744501
申请日：2024-06-14
Inventor： Zhifan FENG , Hua WU , Qiaoqiao SHE , Tian WU
IPC: G06N3/08
CPC classification number: G06N3/08
Abstract: A large model optimization training method in the artificial intelligence fields, such as large models, deep learning, natural language processing, may include: taking, as candidate queries, queries collected from a predetermined data source and capable of serving as input to a large model in response to determining that an optimization triggering condition is met; screening out target queries from the candidate queries, the target queries being queries which cannot be correctly processed by the large model; and constructing respectively corresponding training samples according to the target queries, the training samples being used for carrying out optimization training on the large model.
-
-
-
-
-
Search Results
6
records
(took 0.042 seconds)
