Optimizing LLM Based Retrieval Augmented Generation Pipelines in the Financial Domain
ACL, 2024

Retrieval Augmented Generation (RAG) is a prominent approach in real-word applications for grounding large language model (LLM) generations in up to date and domain-specific knowledge. However, there is a lack of systematic investigations of the impact of each component (retrieval quality, prompts, generation models) on the generation quality of a RAG pipeline in real world scenarios. In this study, we benchmark 6 LLMs in 15 retrieval scenarios exploring 9 prompts over 2 real world financial domain datasets. We thoroughly discuss the impact of each component in RAG pipeline on answer generation quality and formulate specific recommendations for the design of RAG systems.

Included in review: No