Real-time Factuality Assessment from Adversarial Feedback

• URL: http://arxiv.org/abs/2410.14651v3
• Date: Sun, 27 Jul 2025 14:23:31 GMT
• Title: Real-time Factuality Assessment from Adversarial Feedback
• Authors: Sanxing Chen, Yukun Huang, Bhuwan Dhingra,
• Abstract summary: We show that evaluations for assessing the factuality of news from conventional sources result in high accuracies over time for LLM-based detectors.<n>We argue that a proper factuality evaluation dataset should test a model's ability to reason about current events by retrieving and reading related evidence.
• Score: 11.742257531343814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We show that existing evaluations for assessing the factuality of news from conventional sources, such as claims on fact-checking websites, result in high accuracies over time for LLM-based detectors-even after their knowledge cutoffs. This suggests that recent popular false information from such sources can be easily identified due to its likely presence in pre-training/retrieval corpora or the emergence of salient, yet shallow, patterns in these datasets. Instead, we argue that a proper factuality evaluation dataset should test a model's ability to reason about current events by retrieving and reading related evidence. To this end, we develop a novel pipeline that leverages natural language feedback from a RAG-based detector to iteratively modify real-time news into deceptive variants that challenge LLMs. Our iterative rewrite decreases the binary classification ROC-AUC by an absolute 17.5 percent for a strong RAG-based GPT-4o detector. Our experiments reveal the important role of RAG in both evaluating and generating challenging news examples, as retrieval-free LLM detectors are vulnerable to unseen events and adversarial attacks, while feedback from RAG-based evaluation helps discover more deceitful patterns.

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