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

**Authors:** Zooey Nguyen, Anthony Annunziata, Vinh Luong, Sang Dinh, Quynh Le, Anh Hai Ha, Chanh Le, Hong An Phan, Shruti Raghavan, Christopher Nguyen  
**arXiv:** [2404.11792](https://arxiv.org/abs/2404.11792)  
**Date:** 2024-04-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.11792v1/

## Key points

- Domain-specific model fine-tuning and reasoning mechanisms impact Q&A systems powered by LLMs and RAG
- Combining a fine-tuned embedding model with a fine-tuned LLM improves accuracy for RAG compared to generic models
- Reasoning iterations on top of RAG lead to substantial performance gains, bringing Q&A systems closer to human-expert quality
- Innovation can enhance accuracy in LLM-based Q&A workflows and a structured design space is proposed for technical decision-making

## Summary

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In this paper, we investigate the impact of domain-specific model fine-tuning and reasoning mechanisms on Q&A systems powered by LLMs and RAG. Our experiments using the FinanceBench dataset show that combining a fine-tuned embedding model with a fine-tuned LLM results in improved accuracy for RAG compared to generic models, with significant contributions from the embedding model. Additionally, incorporating reasoning iterations on top of RAG leads to substantial performance gains, bringing Q&A systems closer to human-expert quality. We identify areas where innovation can enhance accuracy in LLM-based Q&A workflows and propose a structured design space for technical decision-making. Our findings aim to assist developers and managers in making informed system-design decisions for improved success. Section 2 provides an overview of related work in Q&A AI, focusing on RAG techniques, fine-tuning strategies, and high-level planning and reasoning. In Section 3, we outline a framework for enhancing generic RAG and propose a structured design space for technical decision-making. We also introduce the FinanceBench dataset and discuss the technical configurations tested. Results from our experiments are presented in Section 4. We discuss our findings in Section 5. Finally, in Section 6 we conclude by outlining future research directions. The introduction of Transformer architecture paved the way for advancements in Q&A AI with models like BERT, RoBERTa, and GPT-3 evolving into large language models (LLMs). Challenges such as handling long-form text were addressed through techniques like Longformer and Transformer-XL. The introduction of Retrieval-Augmented Generation (RAG) by Lewis et al. demonstrated its effectiveness in knowledge-intensive NLP tasks by augmenting generative models with retrieved documents for contextually rich answers. Domain-specific fine-tuning has also played a significant role in adapting LLMs to specific contexts. Works like BERT and RoBERTa have shown the efficacy of fine-tuning in non-generic fields while more efficient approaches like adapter layers and model distillation have enhanced domain-specific LLMs practically. As Q&A systems evolve, there is an increasing focus on developing models capable of complex multi-hop reasoning. The integration of reasoning mechanisms into RAG systems has shown promising results towards achieving higher levels of accuracy and human-like performance.