Not All Claims Are Equally Risky: FACTOR for Adaptive Verification in Factual Long-Form Generation
Large Language Models (LLMs) generate fluent long-form text, however, often add unsupported factual claims. Existing verification techniques improve factuality by grounding generation in external evidence. However, the same verification policy usually applies to all claims despite being differences in hallucination risks. We propose \textit{FACTOR} (\textit{FACTuality-Oriented Risk-aware Verification}), an inference-time model that adapts verification criteria according to claim-level uncertainty. FACTOR combines uncertainty estimation, adaptive language inference verification, and candidate re-ranking to allocate verification effort where it is most needed. We evaluate \textit{FACTOR} on...
| Authors | Areeba Hassan, Arooj Kausar, Syeda Kisaa Fatima et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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