AtomEval: Validity-Aware Atomic Evaluation of Adversarial Claim Rewriting in Fact Verification
arXiv:2604.07967v3 Announce Type: replace Abstract: Large language models (LLMs) can rewrite refuted claims to evade evidence-based fact verifiers, but conventional attack success rate (ASR) can be inflated when rewrites change, weaken, or correct the false proposition they are supposed to preserve. We introduce AtomEval, a validity-aware evaluation protocol for fixed-evidence adversarial claim rewriting. AtomEval represents claims as subject--relation--object--modifier (SROM) atoms, applies a one-way preservation gate to separate valid verifier evasion from proposition-changing rewrites, and
The proliferation of advanced LLMs has necessitated increasingly robust evaluation methods for their adversarial capabilities, particularly in critical applications like fact verification.
Sophisticated readers should care because AtomEval proposes a more reliable way to measure the robustness of fact-checking systems against LLM-generated disinformation, directly impacting trust in AI outputs.
The conventional metrics for evaluating adversarial claim rewriting are now recognized as potentially inflated, prompting a need for more nuanced and valid assessment protocols like AtomEval.
- · AI Safety Researchers
- · Fact Verification Systems
- · Trust & Safety Platforms
- · Ethical AI Developers
- · Malicious LLM Actors
- · Unsophisticated AI Evaluators
- · Systems Reliant on Inflated ASR Metrics
AtomEval will lead to more accurate assessments of LLM adversarial attack capabilities in fact verification.
Improved evaluation methods will drive the development of more resilient and robust fact-checking AI systems.
This could contribute to a higher overall trust in information authenticated by AI-powered fact verification, albeit with ongoing arms races.
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