SpanUQ: Span-Level Uncertainty Quantification for Large Language Model Generation

תקציר מקורי באנגליתarXiv:2607.05721v2 Announce Type: replace Abstract: 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 into a single forward pass over LLM hidden states.