Evidence Absence Is Not Evidence Insufficiency: Diagnosing NEI Construction Artifacts in Fact Verification
arXiv:2605.26663v1 Announce Type: new Abstract: Evidence absence is not evidence insufficiency, but fact verification benchmarks can make them observationally similar. The Not Enough Information (NEI) label is often operationalized through different evidence conditions, and that choice silently determines what a verifier learns and what its score can hide. We introduce NEI-CAP, a construction-aware diagnostic protocol for insufficient-evidence evaluation. Each NEI example carries the construction family that produced it; NEI-CAP audits shortcut cues, validates hard cases through human adjudica
The proliferation of AI fact verification systems necessitates more robust evaluation methods to prevent hidden biases and artifacts in their training and performance. This research addresses a critical limitation in current methodologies that could skew AI development trajectories.
A strategic reader should care because accurate fact verification is crucial for the reliability and trustworthiness of AI systems, impacting applications from content moderation to information retrieval and potentially influencing public perception and decision-making. If AI can't verify information robustly, its utility is limited.
The introduction of NEI-CAP provides a standardized diagnostic protocol that changes how AI models for fact verification are evaluated, moving towards a more nuanced understanding of their limitations regarding 'not enough information' scenarios.
- · AI developers focused on trustworthiness
- · Researchers in AI safety and interpretability
- · Platforms needing high-fidelity fact verification
- · AI models with unaddressed 'not enough information' artifacts
- · Benchmarks that don't account for construction-aware diagnostics
Improved diagnostic tools lead to more reliable and transparent fact-verification AI models.
Enhanced trust in AI systems for information analysis and content moderation, potentially reducing misinformation spread.
Broader adoption of rigorous evaluation standards could accelerate the development of truly robust and unbiased AI, influencing policy and public discourse.
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