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Proceedings of the AAAI Conference on Artificial Intelligence

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PHPFND: Detecting Fake News via Post-Hoc Processing of LL...
Jinke Ma, Ji · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Jinke Ma School of Computer Science and Big Data, Heilongjiang University, Harbin, China
  • Jiachen Ma School of Computer Science and Big Data, Heilongjiang University, Harbin, China School of Artificial Intelligence and Computer Science, Shaanxi Normal University, Xi’an, China
  • Wei Zhang School of Computer Science and Big Data, Heilongjiang University, Harbin, China
  • Yong Liu School of Computer Science and Big Data, Heilongjiang University, Harbin, China

DOI:

https://doi.org/10.1609/aaai.v40i1.37050

Abstract

Large Language Models (LLMs) perform excellently in fake news detection tasks, but their outputs are often accompanied by hallucinations, i.e., generated content that is contradictory to facts. Previous studies have mostly mitigated hallucinations through prompt design. However, this paper reveals that regions in news articles which easily induce hallucinations in LLMs correspond closely to the most challenging regions for fake news detectors. In this paper, we propose a fake news detection framework (PHPFND) based on post-hoc processing of LLMs hallucination. Specifically, our framework includes a hallucination detection module (ISHD) based on information structuring that detects three types of hallucinations in LLMs in a targeted manner, and a hallucination-driven feature enhancement mechanism (HDFE) that incorporates hallucination signals as explicit features into sentence-level encoding and feature fusion to guide the model’s attention toward high-risk regions. Experimental results on two mainstream fake news datasets show that our proposed method significantly outperforms LLM-based baselines.

How to Cite

Ma, J., Ma, J., Zhang, W., & Liu, Y. (2026). PHPFND: Detecting Fake News via Post-Hoc Processing of LLMs Hallucination. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 827–835. https://doi.org/10.1609/aaai.v40i1.37050

Issue

Section

AAAI Technical Track on Application Domains I

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