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公开(公告)号：US20220215180A1
公开(公告)日：2022-07-07
申请号：US17643053
申请日：2021-12-07
Inventor： Jianglu Hu , Hehan LI , Huifeng SUN , Shuqi SUN , Yue CHANG , Tingting LI , Hua WU , Haifeng WANG
IPC: G06F40/35 , G06F16/332
Abstract: The disclosure provides a method for generating a dialogue. The method includes: obtaining an input sentence; determining a type of a task-based response sentence that is to be generated, by updating a current dialogue state based on the input sentence; generating the task-based response sentence by inputting the input sentence into a task-based dialogue response generator; and determining the task-based response sentence as a target response sentence in response to the type of the task-based response sentence being a designated type.
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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.
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33.发明申请公开(公告)号：US20250103963A1
公开(公告)日：2025-03-27
申请号：US18969634
申请日：2024-12-05
IPC: G06N20/00
Abstract: A method for processing a query-response information is provided, which relates to a field of artificial intelligence technology, and in particular to fields of deep learning, large models, intelligent query and response, etc. The method for processing a query-response information includes: generating at least one initial response information according to a query information provided by an object; acquiring at least one feedback information corresponding to the at least one initial response information, wherein the feedback information indicates a preference degree of the object for the initial response information; and generating a training sample according to the query information, the at least one initial response information and the at least one feedback information. The present disclosure further provides a method for training a conversational model, an electronic device, and a storage medium.
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34.发明申请公开(公告)号：US20250094789A1
公开(公告)日：2025-03-20
申请号：US18968810
申请日：2024-12-04
Inventor： Hua LU , Shilong FAN , Zeyang LEI , Bingjin CHEN , Siqi BAO , Hua WU
IPC: G06N3/0475
Abstract: A method for evaluating a large model, an electronic device and a computer readable storage medium are provided, which relate to a field of artificial intelligence technology, and in particular to fields of large models technology and deep learning technology. The method includes: evaluating a response information of each of M large language models for an input instruction based on a preset evaluation rule, so as to obtain a first evaluation information for each response information, where M is a positive integer greater than 1; evaluating, in response to the first evaluation information for the M large language models being consistent with each other, each response information in a plurality of evaluation dimensions, so as to obtain a second evaluation information for each response information; and determining an evaluation result representing a responsiveness of each large language model, according to the second evaluation information for each response information.
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公开(公告)号：US20240412002A1
公开(公告)日：2024-12-12
申请号：US18747641
申请日：2024-06-19
Inventor： Yanbin ZHAO , Siyu DING , Shuohuan WANG , Yu SUN , Hao TIAN , Hua WU , Haifeng WANG
IPC: G06F40/35
Abstract: A method is provided. The method includes: obtaining a first sample dataset; inputting at least one first question text corresponding to at least one piece of first sample data into a dialog model separately to obtain at least one first answer prediction result; inputting each second question text into the dialog model to obtain a second answer prediction result output by the dialog model; inputting the second answer prediction result into a reward model to obtain a score of the second answer prediction result output by the reward model; determining a comprehensive loss based on the at least one first answer prediction result, a first answer text of each of the at least one piece of first sample data, and a score corresponding to each of at least one piece of second sample data; and adjusting at least one parameter of the dialog model based on the comprehensive loss.
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Patent Agency Ranking
公开(公告)号：US20230101401A1
公开(公告)日：2023-03-30
申请号：US18056197
申请日：2022-11-16
Inventor： Ruiqing ZHANG , Zhongjun HE , Zhi LI , Hua WU
IPC: G06F40/232 , G06F40/279 , G06F40/53
Abstract: A text processing method is provided. The method includes: a first probability value of each candidate character of a plurality of candidate characters corresponding to a target position is determined based on character feature information corresponding to the target position in a text fragment to be processed, wherein the character feature information is determined based on a context at the target position in the text fragment to be processed; a second probability value of each candidate character of the plurality of candidate characters is determined based on a character string including the candidate character and at least one character in at least one position in the text fragment to be processed adjacent to the target position; and a correction character at the target position is determined based on the first probability value and the second probability value of each candidate character of the plurality of candidate characters.
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公开(公告)号：US20230095352A1
公开(公告)日：2023-03-30
申请号：US18074853
申请日：2022-02-05
Inventor： Ruiqing ZHANG , Hui LIU , Zhongjun HE , Zhi LI , Hua WU
IPC: G06N3/0455 , G06F40/44 , G06F40/58 , G06N3/08 , G06N3/042
Abstract: The present disclosure provides a translation method and apparatus, an electronic device, and a non-transitory storage medium. An implementation includes: determining an encoded feature of a sentence to be translated by an encoding module; determining, by a graph network module, a knowledge fusion feature of the sentence to be translated based on a preset graph network, wherein the preset graph network is constructed based on a polysemous word in a source language corresponding to the sentence to be translated and a plurality of translated words corresponding to the polysemous word in a target language; determining, by a decoding network, a translated sentence corresponding to the sentence to be translated based on the encoded feature and the knowledge fusion feature.
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38.发明申请公开(公告)号：US20230092736A1
公开(公告)日：2023-03-23
申请号：US17872318
申请日：2022-07-25
Inventor： Wenbin JIANG , Yajuan LYU , Yong ZHU , Hua WU , Haifeng WANG
Abstract: The present disclosure provides a method for processing intelligent question-answering, an intelligent question-answering system, an electronic device and a storage medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, natural language processing technologies, or the like. An implementation includes: acquiring an input question and input data information; and based on the question, the data information and a plurality of knowledge bases, deciding an answer to the question by multilayer appreciation using a plurality of understanding module layers.
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公开(公告)号：US20230090625A1
公开(公告)日：2023-03-23
申请号：US18053034
申请日：2022-11-07
Inventor： Ruiqing ZHANG , Zhongjun HE , Hua WU
IPC: G06F40/279 , G06F40/166
Abstract: Disclosed are a method for correcting a text, an electronic device and a storage medium. The method includes: acquiring a text to be corrected; acquiring a phonetic symbol sequence of the text to be corrected; and obtaining a corrected text by inputting the text to be corrected and the phonetic symbol sequence into a text correction model, in which, the text correction model obtains the corrected text by: detecting an error word in the text to be corrected, determining a phonetic symbol corresponding to the error word in the phonetic symbol sequence, and adding the phonetic feature corresponding to the phonetic symbol behind the error word to obtain a phonetic symbol text, and correcting the error word and the phonetic feature in the phonetic symbol text to obtain the corrected text.
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公开(公告)号：US20230088360A1
公开(公告)日：2023-03-23
申请号：US18059389
申请日：2022-11-28
Inventor： Pengzhi GAO , Zhongjun HE , Zhi LI , Hua WU
IPC: G06F40/40 , G06F40/166 , G06N20/00
Abstract: A method of training a deep learning model is provided, which relates to a field of artificial intelligence, in particular to a field of a natural language processing technology and a field of a machine translation technology. A specific implementation solution includes: processing sample source data and corresponding sample target data respectively by using the deep learning model, so as to obtain a first output value and a second output value; determining a regularization function value according to the first output value and the second output value; and adjusting a parameter of the depth learning model according to the regularization function value, so as to obtain a pre-trained depth learning model. A method of processing text data, an electronic device, and a storage medium are further provided.
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