View source for 18744501. TRAINING SAMPLE ACQUIRING METHOD AND APPARATUS AS WELL AS LARGE MODEL OPTIMIZATION TRAINING METHOD AND APPARATUS simplified abstract (Beijing Baidu Netcom Science Technology Co., Ltd.)

=TRAINING SAMPLE ACQUIRING METHOD AND APPARATUS AS WELL AS LARGE MODEL OPTIMIZATION TRAINING METHOD AND APPARATUS=


==Organization Name==

[[:Category:Beijing Baidu Netcom Science Technology Co., Ltd.|Beijing Baidu Netcom Science Technology Co., Ltd.]]


[[Category:Beijing Baidu Netcom Science Technology Co., Ltd.]]

==Inventor(s)==

[[:Category:Zhifan Feng of Beijing (CN)|Zhifan Feng of Beijing (CN)]][[Category:Zhifan Feng of Beijing (CN)]]

[[:Category:Hua Wu of Beijing (CN)|Hua Wu of Beijing (CN)]][[Category:Hua Wu of Beijing (CN)]]

[[:Category:Qiaoqiao She of Beijing (CN)|Qiaoqiao She of Beijing (CN)]][[Category:Qiaoqiao She of Beijing (CN)]]

[[:Category:Tian Wu of Beijing (CN)|Tian Wu of Beijing (CN)]][[Category:Tian Wu of Beijing (CN)]]

==TRAINING SAMPLE ACQUIRING METHOD AND APPARATUS AS WELL AS LARGE MODEL OPTIMIZATION TRAINING METHOD AND APPARATUS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18744501 titled 'TRAINING SAMPLE ACQUIRING METHOD AND APPARATUS AS WELL AS LARGE MODEL OPTIMIZATION TRAINING METHOD AND APPARATUS

'''Simplified Explanation''': The patent application describes a method for optimizing large models in artificial intelligence fields by selecting candidate queries, filtering out queries that cannot be processed correctly, and creating training samples for optimization training.

'''Key Features and Innovation''':
* Method for selecting candidate queries for large model optimization training.
* Screening out queries that cannot be processed correctly by the large model.
* Creating training samples based on target queries for optimization training.

'''Potential Applications''': This technology can be applied in various fields such as deep learning, natural language processing, and other artificial intelligence applications that involve large models.

'''Problems Solved''': This technology addresses the challenge of efficiently optimizing large models by selecting appropriate queries for training and filtering out irrelevant queries.

'''Benefits''': The method outlined in the patent application can improve the performance and accuracy of large models in artificial intelligence applications, leading to better results and more efficient training processes.

'''Commercial Applications''':
Title: Optimization Training Method for Large Models in Artificial Intelligence
Description: This technology can be used in industries such as healthcare, finance, and e-commerce to enhance data analysis, customer service, and decision-making processes.

'''Prior Art''': Researchers can explore prior art related to large model optimization methods, query selection techniques, and training sample construction in artificial intelligence.

'''Frequently Updated Research''': Stay updated on the latest advancements in large model optimization, deep learning, and natural language processing to enhance the effectiveness of this technology.

'''Questions about Large Model Optimization''':
1. How does the method in the patent application improve the optimization training of large models?
2. What are the potential implications of this technology in industries beyond artificial intelligence?


==Original Abstract Submitted==

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.



[[Category:G06N3/08]]
[[Category:CPC_G06N3/08]]