Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study
<|reference_start|>Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study: 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.<|reference_end|>
@article{nguyen2024enhancing,
    title={Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning:
  A Comparative Study},
    author={Zooey Nguyen, Anthony Annunziata, Vinh Luong, Sang Dinh, Quynh Le, Anh
  Hai Ha, Chanh Le, Hong An Phan, Shruti Raghavan, Christopher Nguyen},
    journal={arXiv preprint arXiv:2404.11792},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11792},
    primaryClass={cs.AI}
}