Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection

Herun Wan, Jiaying Wu, Minnan Luo, Zhi Zeng, Zhixiong Su

Keywords: Shortcut Learning, Injection Attack, Misinformation Detection

TL;DR: This paper measures and mitigates shortcut leaning in the misinformation detection.

Abstract: Misinformation detectors often rely on superficial cues (i.e., shortcuts) that correlate with misinformation in training data but fail to generalize to the diverse and evolving nature of real-world misinformation. This issue is exacerbated by large language models (LLMs), which can easily generate convincing misinformation using simple prompts. We introduce TruthOverTricks, a unified evaluation paradigm for measuring shortcut learning in misinformation detection. TruthOverTricks categorizes shortcut behaviors into intrinsic shortcut induction and extrinsic shortcut injection, and evaluates seven representative detectors across 14 popular benchmarks, along with two new factual misinformation datasets, NQ-Misinfo and Streaming-Misinfo. Empirical results reveal that existing detectors suffer severe performance degradation when exposed to both naturally occurring and adversarially crafted shortcuts. To address this, we propose the Shortcut Mitigation Framework (SMF), an LLM-augmented data augmentation framework that mitigates shortcut reliance through paraphrasing, factual summarization, and sentiment normalization. SMF consistently enhances robustness across 16 benchmarks, forcing models to rely on deeper semantic understanding rather than shortcut cues.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 21440