Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study
This work addresses enhancing Q&A systems for domain-specific applications like finance, but it is incremental as it builds on existing RAG and fine-tuning methods.
This paper tackled improving question-answering systems by fine-tuning models and using iterative reasoning on financial data, finding that fine-tuned embedding models and reasoning iterations significantly boost accuracy, with the latter bringing performance closer to human-expert quality.
This paper investigates the impact of domain-specific model fine-tuning and of reasoning mechanisms on the performance of question-answering (Q&A) systems powered by large language models (LLMs) and Retrieval-Augmented Generation (RAG). Using the FinanceBench SEC financial filings dataset, we observe that, for RAG, combining a fine-tuned embedding model with a fine-tuned LLM achieves better accuracy than generic models, with relatively greater gains attributable to fine-tuned embedding models. Additionally, employing reasoning iterations on top of RAG delivers an even bigger jump in performance, enabling the Q&A systems to get closer to human-expert quality. We discuss the implications of such findings, propose a structured technical design space capturing major technical components of Q&A AI, and provide recommendations for making high-impact technical choices for such components. We plan to follow up on this work with actionable guides for AI teams and further investigations into the impact of domain-specific augmentation in RAG and into agentic AI capabilities such as advanced planning and reasoning.