SpanUQ: Span-Level Uncertainty Quantification for Large Language Model Generation
Uncertainty estimation is essential not only for the trustworthy deployment of large language models (LLMs) but also as a foundation for self-refinement in LLM generation. However, existing approaches operate at suboptimal granularities: token-level scores lack semantic coherence, while sequence-level scores fail to localize errors. We formalize Span-Level Uncertainty Estimation (SLUE), a new task that targets the natural granularity for uncertainty: semantically coherent text spans, each conveying a single assessable unit of meaning. To address this task, we introduce SPANUQ, a lightweight (25M parameter) probe that distills the uncertainty knowledge from expensive multi-sample inference...
| Authors | Yimeng Zhang, Yingying Zhuang, Ziyi Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 July 2026 |
| Last updated at source | 29 September 2026 |
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