Mitigating Prompt-Induced Hallucinations in Large Language Models via Structured Reasoning

• URL: http://arxiv.org/abs/2601.02739v1
• Date: Tue, 06 Jan 2026 06:02:45 GMT
• Title: Mitigating Prompt-Induced Hallucinations in Large Language Models via Structured Reasoning
• Authors: Jinbo Hao, Kai Yang, Qingzhen Su, Yang Chen, Yifan Li, Chao Jiang,
• Abstract summary: We introduce a code module to guide knowledge-graph exploration and incorporate code as part of the chain-of-thought prompt.<n>We empirically evaluate the proposed approach using GPT-4 and LLaMA-3.3 on multiple public datasets.
• Score: 11.278137554160383
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To address hallucination issues in large language models (LLMs), this paper proposes a method for mitigating prompt-induced hallucinations. Building on a knowledge distillation chain-style model, we introduce a code module to guide knowledge-graph exploration and incorporate code as part of the chain-of-thought prompt, forming an external knowledge input that provides more accurate and structured information to the model. Based on this design, we develop an improved knowledge distillation chain-style model and leverage it to analyze and constrain the reasoning process of LLMs, thereby improving inference accuracy. We empirically evaluate the proposed approach using GPT-4 and LLaMA-3.3 on multiple public datasets. Experimental results demonstrate that incorporating code modules significantly enhances the model's ability to capture contextual information and effectively mitigates prompt-induced hallucinations. Specifically, HIT@1, HIT@3, and HIT@5 improve by 15.64%, 13.38%, and 13.28%, respectively. Moreover, the proposed method achieves HIT@1, HIT@3, and HIT@5 scores exceeding 95% across several evaluation settings. These results indicate that the proposed approach substantially reduces hallucination behavior while improving the accuracy and verifiability of large language models.

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