Improving Retrieval for RAG based Question Answering Models on Financial Documents
This addresses accuracy issues in financial document QA systems, but the approach is incremental as it builds on existing RAG methods.
The paper tackled the problem of suboptimal text chunk retrieval in Retrieval Augmented Generation (RAG) pipelines for question answering on financial documents, and found that implementing strategies like sophisticated chunking and query expansion can substantially improve retrieval quality and LLM performance.
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 capabilities of LLMs. To augment the efficacy of LLMs, it is crucial to refine the RAG process. This paper explores the existing constraints of RAG pipelines and introduces methodologies for enhancing text retrieval. It delves into strategies such as sophisticated chunking techniques, query expansion, the incorporation of metadata annotations, the application of re-ranking algorithms, and the fine-tuning of embedding algorithms. Implementing these approaches can substantially improve the retrieval quality, thereby elevating the overall performance and reliability of LLMs in processing and responding to queries.