SeSE: Black-Box Uncertainty Quantification for Large Language Models Based on Structural Information Theory
arXiv:2511.16275v4 Announce Type: replace Abstract: Reliable uncertainty quantification (UQ) is essential for deploying large language models (LLMs) in safety-critical scenarios, as it enables them to abstain from responding when uncertain, thereby avoiding hallucinations, i.e., plausible yet factually incorrect responses. However, while semantic UQ methods have achieved advanced performance, they overlook latent semantic structural information that could enable more precise uncertainty estimates. In this paper, we propose \underline{Se}mantic \underline{S}tructural \underline{E}ntropy ({SeSE}
The increasing deployment of LLMs in critical applications necessitates robust uncertainty quantification to mitigate risks like hallucinations, making advanced UQ methods a timely development.
Improved uncertainty quantification for LLMs allows for safer and more reliable deployment in sensitive areas, fostering trust and enabling broader adoption of AI agents.
The ability of LLMs to abstain from responding when uncertain, based on structural information, significantly enhances their reliability and trustworthiness in practical scenarios.
- · AI developers
- · Companies deploying LLMs
- · Safety-critical industries
- · AI ethics and safety researchers
- · LLM competitors with less robust UQ
- · Sectors reliant on manual verification of LLM outputs
LLMs can be deployed in more high-stakes environments due to reduced hallucination risk.
Increased user and regulatory confidence in AI systems leads to faster integration of LLM-powered applications across industries.
The enhanced reliability could accelerate the development and adoption of fully autonomous AI agents, reshaping white-collar workflows.
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