Roy, K.; Zi, Y.; Shyalika, C.; Prasad, R.; Murali, S.; Palit, V.; Sheth, A. QA-RAG: Leveraging Question and Answer-based Retrieved Chunk Re-Formatting for Improving Response Quality During Retrieval-augmented Generation. Preprints2024, 2024070376. https://doi.org/10.20944/preprints202407.0376.v1
APA Style
Roy, K., Zi, Y., Shyalika, C., Prasad, R., Murali, S., Palit, V., & Sheth, A. (2024). QA-RAG: Leveraging Question and Answer-based Retrieved Chunk Re-Formatting for Improving Response Quality During Retrieval-augmented Generation. Preprints. https://doi.org/10.20944/preprints202407.0376.v1
Chicago/Turabian Style
Roy, K., Vedant Palit and Amit Sheth. 2024 "QA-RAG: Leveraging Question and Answer-based Retrieved Chunk Re-Formatting for Improving Response Quality During Retrieval-augmented Generation" Preprints. https://doi.org/10.20944/preprints202407.0376.v1
Abstract
The use of Retrieval-augmented generation (RAG) using large language models (LLMs) has shown potential for addressing issues such as hallucinations and inadequately contextualized responses. A pivotal stage in the RAG process involves a retriever for retrieving chunks based on semantic similarity with the query. In this study, we advocate for and provide experimental evidence supporting integrating and maintaining questions and answers (QA) formatted databases to improve retrieved-context representations and response quality. Our experiments evaluate our approach on benchmark RAG datasets using standard evaluation metrics and provide comparative analyses against state-of-the-art retrieval methods, showing the potential of our approach.
Keywords
Retrieval-augmented Generation; Information Retrieval; Large Language Models
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
