Optimizing Query Generation for Enhanced Document Retrieval in RAG
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866929424281108480 |
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| author | Koo, Hamin Kim, Minseon Hwang, Sung Ju |
| author_facet | Koo, Hamin Kim, Minseon Hwang, Sung Ju |
| contents | Large Language Models (LLMs) excel in various language tasks but they often generate incorrect information, a phenomenon known as "hallucinations". Retrieval-Augmented Generation (RAG) aims to mitigate this by using document retrieval for accurate responses. However, RAG still faces hallucinations due to vague queries. This study aims to improve RAG by optimizing query generation with a query-document alignment score, refining queries using LLMs for better precision and efficiency of document retrieval. Experiments have shown that our approach improves document retrieval, resulting in an average accuracy gain of 1.6%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12325 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Query Generation for Enhanced Document Retrieval in RAG Koo, Hamin Kim, Minseon Hwang, Sung Ju Information Retrieval Large Language Models (LLMs) excel in various language tasks but they often generate incorrect information, a phenomenon known as "hallucinations". Retrieval-Augmented Generation (RAG) aims to mitigate this by using document retrieval for accurate responses. However, RAG still faces hallucinations due to vague queries. This study aims to improve RAG by optimizing query generation with a query-document alignment score, refining queries using LLMs for better precision and efficiency of document retrieval. Experiments have shown that our approach improves document retrieval, resulting in an average accuracy gain of 1.6%. |
| title | Optimizing Query Generation for Enhanced Document Retrieval in RAG |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2407.12325 |