From Misleading Queries to Accurate Answers: A Three-Stage Fine-Tuning Method for LLMs

• URL: http://arxiv.org/abs/2504.11277v1
• Date: Tue, 15 Apr 2025 15:16:45 GMT
• Title: From Misleading Queries to Accurate Answers: A Three-Stage Fine-Tuning Method for LLMs
• Authors: Guocong Li, Weize Liu, Yihang Wu, Ping Wang, Shuaihan Huang, Hongxia Xu, Jian Wu,
• Abstract summary: Large language models (LLMs) exhibit excellent performance in natural language processing (NLP)<n>Existing methods focus on correcting the output, but they often overlook the potential of improving the ability of LLMs to detect and correct misleading content in the input itself.<n>We propose a novel three-stage fine-tuning method that enhances the ability of LLMs to detect and correct misleading information in the input.
• Score: 5.23164145730825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) exhibit excellent performance in natural language processing (NLP), but remain highly sensitive to the quality of input queries, especially when these queries contain misleading or inaccurate information. Existing methods focus on correcting the output, but they often overlook the potential of improving the ability of LLMs to detect and correct misleading content in the input itself. In this paper, we propose a novel three-stage fine-tuning method that enhances the ability of LLMs to detect and correct misleading information in the input, further improving response accuracy and reducing hallucinations. Specifically, the three stages include (1) training LLMs to identify misleading information, (2) training LLMs to correct the misleading information using built-in or external knowledge, and (3) training LLMs to generate accurate answers based on the corrected queries. To evaluate our method, we conducted experiments on three datasets for the hallucination detection task and the question answering (QA) task, as well as two datasets containing misleading information that we constructed. The experimental results demonstrate that our method significantly improves the accuracy and factuality of LLM responses, while also enhancing the ability to detect hallucinations and reducing the generation of hallucinations in the output, particularly when the query contains misleading information. We will publicly release our code upon acceptance.

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