Summary of Enhancing Q&a with Domain-specific Fine-tuning and Iterative Reasoning: a Comparative Study, by Zooey Nguyen et al.
Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study
by Zooey Nguyen, Anthony Annunziata, Vinh Luong, Sang Dinh, Quynh Le, Anh Hai Ha, Chanh Le, Hong An Phan, Shruti Raghavan, Christopher Nguyen
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the effects of fine-tuning large language models (LLMs) and Retrieval-Augmented Generation (RAG) on question-answering (Q&A) systems. The study uses the FinanceBench SEC financial filings dataset to examine how domain-specific model fine-tuning and reasoning mechanisms impact performance. Results show that combining a fine-tuned embedding model with a fine-tuned LLM yields better accuracy for RAG, with greater gains attributed to fine-tuned embedding models. Furthermore, incorporating reasoning iterations on top of RAG leads to an even larger performance increase, allowing Q&A systems to approach human-expert quality. The findings have implications for AI development and are discussed in the context of a proposed technical design space for Q&A AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well question-answering (Q&A) computer systems can answer questions using large language models (LLMs). They tested different ways to fine-tune these LLMs and found that it makes a big difference. When they combined fine-tuned models, the Q&A system got even better, almost as good as a human expert! This study is important because it helps us understand how to make AI systems smarter and more accurate. |
Keywords
» Artificial intelligence » Embedding » Fine tuning » Question answering » Rag » Retrieval augmented generation
