Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study

TL;DR

This study demonstrates that domain-specific fine-tuning and iterative reasoning significantly improve the accuracy of large language model-based Q&A systems, especially in financial domains, approaching human expert quality.

Contribution

It provides a comparative analysis of fine-tuning and reasoning techniques in RAG-based Q&A systems, highlighting their combined impact on performance and offering a structured design space for future development.

Findings

Fine-tuned embedding models enhance RAG accuracy more than generic models.

Reasoning iterations substantially improve Q&A performance.

Domain-specific augmentation is crucial for high-quality financial Q&A.

Abstract

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.