Research on large model table question answering method based on thought of chain and vector matching filtering

Haichao Tang; Licai Wang; Xun Hu; Qibin Luo

doi:10.1117/12.3033063

5 July 2024 Research on large model table question answering method based on thought of chain and vector matching filtering

Haichao Tang, Licai Wang, Xun Hu, Qibin Luo

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Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131841X (2024) https://doi.org/10.1117/12.3033063
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Implementing table-based question answering can fully leverage the rich information in tabular data, and the development of large language models has provided strong natural language processing and understanding capabilities. Current research on table-based question answering with large language models mainly focuses on how to improve text-to-SQL abilities and the development of table pre-training models, lacking in studies on feasible technical approaches for applying general large language models to engineering applications involving large-scale database table question answering. In response, combining techniques such as prompt learning and chain of thought，this paper constructs a database question answering chain of thought based on large language model, proposes a table filtering scheme based on vectorized matching of table structural information, specifically addressing poor table filtering effects, and constructs a database optimization chain of thought in the context of de-identification scenarios. Experimental verification shows that after adopting the chain of thought construction and vector matching filtering scheme, the database question answering capability of the large model has been effectively improved.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Haichao Tang, Licai Wang, Xun Hu, and Qibin Luo "Research on large model table question answering method based on thought of chain and vector matching filtering", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131841X (5 July 2024); https://doi.org/10.1117/12.3033063

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KEYWORDS

Databases

Data modeling

Tunable filters

Commercial off the shelf technology

Performance modeling

Semantics

Data storage

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