mlsanityData to 14 Oct 2026

GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models

arXiv 2509.07925 ↗First posted 9 Sep 2025cs.CL cs.AI cs.LGcited by 3

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Abstract (arXiv)

Uncertainty estimation is essential for enhancing the reliability of Large Language Models (LLMs), particularly in high-stakes applications. Existing methods often overlook semantic dependencies, relying on token-level probability measures that fail to capture structural relationships within the generated text. We propose GENUINE: Graph ENhanced mUlti-level uncertaINty Estimation for Large Language Models, a structure-aware framework that leverages dependency parse trees and hierarchical graph pooling to refine uncertainty quantification. By incorporating supervised learning, GENUINE effectively models semantic and structural relationships, improving confidence assessments. Extensive experiments across NLP tasks show that GENUINE achieves up to 29% higher AUROC than semantic entropy-based approaches and reduces calibration errors by over 15%, demonstrating the effectiveness of graph-based uncertainty modeling. The code is available at https://github.com/ODYSSEYWT/GUQ.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 25 of its 58 references that are in the map. First posted on arXiv 9 Sep 2025.

Builds on

25 of 58 references.

References · 25

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13 more
  1. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 401
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026A Systematic Evaluation of Black-Box Uncertainty Estimation Methods for Large Language Modelscited by 2
  2. 2026Position: Uncertainty Quantification in LLMs is Just Unsupervised Clusteringcited by 0
  3. 2026Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Searchcited by 0