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

• URL: http://arxiv.org/abs/2404.11792v2
• Date: Fri, 19 Apr 2024 20:28:16 GMT
• Title: 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,
• Abstract summary: 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. We 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.
• Score: 0.3932300766934226
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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