SpanUQ Introduces Span-Level Uncertainty Estimation for LLMs

1 reports archived1 independent sourcesupdated 10/22/2026, 18:56:00

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The NeurIPS 2026 paper 'SpanUQ' advances LLM uncertainty estimation from token-level to semantic span-level granularity, precisely identifying unreliable content within responses. This approach achieves 10-20x faster inference compared to traditional sampling methods, providing finer-grained confidence signals for downstream systems and multi-agent collaboration.

LATEST/The NeurIPS 2026 paper 'SpanUQ' advances LLM uncertainty estimation from token-level to semantic span-level granularity, precisely identifying unreliable content within responses. This approach achieves 10-20x faster inference compared to traditional sampling methods, providing finer-grained confidence signals for downstream systems and multi-agent collaboration.

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机器之心 (微信公众号)T2·Oct 22, 18:5678 pts
SpanUQ Introduces Span-Level Uncertainty Estimation for LLMs
- Defines new task SLUE: Focuses uncertainty estimation on semantic spans rather than single tokens or whole texts
- Performance Breakthrough: Achieves 10-20x faster inference compared to traditional multiple-sampling methods
- Application Value: Provides transferable, decision-ready fine-grained confidence signals for Multi-Agent systems
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