Calibrated Selective Fact-Checking via Evidence Chain Evaluation
Large language models (LLMs) can achieve strong fact-checking accuracy, yet forced binary decisions conceal a critical reliability problem: systems may issue confident verdicts even when supporting evidence is weak, sparse, or internally inconsistent. We address this issue through Evidence Chain Evaluation (ECE), a selective fact-checking framework that permits abstention via an uncertain verdict instead of requiring a true/false decision for every claim. The evaluated system is a tool-using verification agent that gathers evidence through web search, scholarly search, and executable checks, and then returns a structured verdict with confidence and source-level metadata. On ECE-Bench, ECE...
| Authors | Dekun Yang |
|---|---|
| Primary category | cs.AI |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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