# SeSE: Black-Box Uncertainty Quantification for Large Language Models Based on Structural Information Theory

arXiv: https://arxiv.org/abs/2511.16275 · HTML: https://mlsanity.com/paper/2511.16275/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2025.

## Built on by (most cited first, 7 of 7)

- [The Six Sigma Agent: Achieving Enterprise-Grade Reliability in LLM Systems Through Consensus-Driven Decomposed Execution](https://mlsanity.com/paper/2601.22290/) (2026)
- [FaithSCAN: Model-Driven Single-Pass Hallucination Detection for Faithful Visual Question Answering](https://mlsanity.com/paper/2601.00269/) (2026)
- [Understanding Why Language Models Hallucinate: Testing Reasoning Against Priors](https://mlsanity.com/paper/2607.00447/) (2026)
- [Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering](https://mlsanity.com/paper/2605.19220/) (2026)
- [Beyond Semantic Equivalence: Logical Graphs for LLM Uncertainty Quantification](https://mlsanity.com/paper/2607.16868/) (2026)
- [When the Wrong Key Wins: Understanding and Detecting Hallucinations in LLMs](https://mlsanity.com/paper/2609.15106/) (2026)
- [Jailbreaks for Black-Box Uncertainty Quantification in Large Reasoning Models](https://mlsanity.com/paper/2609.35350/) (2026)

## Abstract (arXiv)

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 underlineSemantic underlineStructural underlineEntropy (SeSE), a principled black-box UQ framework applicable to both open- and closed-source LLMs. To reveal the intrinsic structure of the semantic space, SeSE constructs its optimal hierarchical abstraction through an encoding tree with minimal structural entropy. The structural entropy of this encoding tree thus quantifies the inherent uncertainty within LLM semantic space after optimal compression. Additionally, unlike existing methods that primarily focus on simple short-form generation, we extent SeSE to provide interpretable, granular uncertainty estimation for long-form outputs. We theoretically prove that SeSE generalizes semantic entropy, the gold standard for UQ in LLMs, and empirically demonstrate its superior performance over strong baselines across 24 model-dataset combinations.