arXiv:2607.05721v1 Announce Type: new 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
The rapid deployment of LLMs highlights the urgent need for robust uncertainty quantification to ensure their reliability and trustworthiness, especially as applications become more critical.
Span-level uncertainty quantification improves the interpretability and self-correction capabilities of LLMs, which is crucial for building more reliable AI systems and accelerating their integration into sensitive applications.
Existing methods for assessing LLM reliability (token or sequence level) are being augmented by a new, more semantically coherent approach that can localize errors more effectively.
- · AI developers
- · LLM users (especially enterprises)
- · AI safety researchers
- · Software quality assurance
- · LLMs without robust uncertainty quantification
- · Applications requiring high-stakes decision-making
More trustworthy and robust LLM applications become feasible.
Increased adoption of LLMs in industries where explainability and reliability are paramount.
Reduced regulatory friction for LLM deployment due to better inherent safety mechanisms.
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Read at arXiv cs.CL