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

TL;DR

This paper introduces a structured reasoning method using code modules and knowledge graphs to significantly reduce hallucinations in large language models, enhancing their accuracy and reliability.

Contribution

It presents a novel approach combining code-guided knowledge exploration with knowledge distillation to mitigate prompt-induced hallucinations in LLMs.

Findings

Improved HIT@1, HIT@3, and HIT@5 scores by over 13%

Achieved over 95% HIT@1, HIT@3, and HIT@5 in multiple settings

Effectively reduces hallucination while enhancing inference accuracy

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.