Improving Retrieval for RAG based Question Answering Models on Financial Documents

arXiv:2404.07221v1 Announce Type: cross
Abstract: The effectiveness of Large Language Models (LLMs) in generating accurate responses relies heavily on the quality of input provided, particularly when employing Retrieval Augmented Generation (RAG) techniques. RAG enhances LLMs by sourcing the most relevant text chunk(s) to base queries upon. Despite the significant advancements in LLMs' response quality in recent years, users may still encounter inaccuracies or irrelevant answers; these issues often stem from suboptimal text chunk retrieval by RAG rather than the inherent …

abstract arxiv cs.cl cs.ir cs.lg documents financial improving language language models large language large language models llms q-fin.gn quality queries question question answering rag responses retrieval retrieval augmented generation text type