Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2505.10792v2
• Date: Mon, 19 May 2025 01:31:13 GMT
• Title: Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation
• Authors: Zhan Peng Lee, Andre Lin, Calvin Tan,
• Abstract summary: Finetune-RAG is a training dataset constructed to mimic real-world imperfections.<n>Finetune-RAG improves factual accuracy by 21.2% over the base model.<n> Bench-RAG is an evaluation pipeline that stress tests models under realistic imperfect retrieval scenarios.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) has emerged as a powerful framework to improve factuality in large language models (LLMs) by grounding their outputs in retrieved documents. However, ensuring perfect retrieval of relevant information remains challenging, and when irrelevant content is passed downstream to an LLM, it can lead to hallucinations. In this work, we propose Finetune-RAG, a simple and effective fine-tuning approach that features the first-of-its-kind RAG training dataset constructed to mimic real-world imperfections. Experimental results show that Finetune-RAG improves factual accuracy by 21.2% over the base model. We also propose Bench-RAG, an LLM-as-a-judge evaluation pipeline that stress tests models under realistic imperfect retrieval scenarios. Our codebase and dataset are fully open sourced for community use.

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