A retrieval-enhanced generative inference method based on large language models

Shanjin Bai; Qiu Cheng; Zhongke Zhu; Yongjin Zhang; Cunyi Wang

doi:10.1117/12.3044973

30 September 2024 A retrieval-enhanced generative inference method based on large language models

Shanjin Bai, Qiu Cheng, Zhongke Zhu, Yongjin Zhang, Cunyi Wang

Proceedings Volume 13286, Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024); 132861J (2024) https://doi.org/10.1117/12.3044973
Event: Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), 2024, Guangzhou, China

Abstract

Chat models, such as ChatGPT, have shown outstanding performance in understanding and responding to human instructions, with far-reaching impacts on natural language questioning and answering. However, in vertical domains, harmful false facts can be generated due to the lack of targeted optimization during question answering. To address the generation of harmful false facts and improve the accuracy rate of large language models in answering questions in vertical domains, this paper proposes a retrieval-enhanced generative inference method based on large language models. Firstly, utilizing large language models to extract entity information from question sentences, enhancing database retrieval; secondly, through semantic encoding and embedding vectorization of question sentences, enhancing knowledge base retrieval; finally, we propose a new technique called self-consistency prompt word engineering, and strengthening the retrieval results, generating multiple independent thought chains, and summarizing to obtain the highest consistency answer. Compared with existing benchmark models on a self-built dataset, the experimental results show that the retrieval-enhanced generative inference method based on large language models proposed in this paper can effectively improve the accuracy of large language models in answering questions in the vertical domains.

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

Citation Download Citation

Shanjin Bai, Qiu Cheng, Zhongke Zhu, Yongjin Zhang, and Cunyi Wang "A retrieval-enhanced generative inference method based on large language models", Proc. SPIE 13286, Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), 132861J (30 September 2024); https://doi.org/10.1117/12.3044973

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