# Enhancing Uncertainty Modeling with Semantic Graph for Hallucination Detection

arXiv: https://arxiv.org/abs/2501.02020 · HTML: https://mlsanity.com/paper/2501.02020/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 22 of 22)

- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics](https://mlsanity.com/paper/2104.13346/) (2021)
- [A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation](https://mlsanity.com/paper/2307.03987/) (2023)
- [Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity](https://mlsanity.com/paper/2310.07521/) (2023)
- [Language Model Cascades: Token-level uncertainty and beyond](https://mlsanity.com/paper/2404.10136/) (2024)
- [Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus](https://mlsanity.com/paper/2311.13230/) (2023)
- [SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency](https://mlsanity.com/paper/2311.01740/) (2023)
- [A Survey of Large Language Models in Finance (FinLLMs)](https://mlsanity.com/paper/2402.02315/) (2024)
- [What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability](https://mlsanity.com/paper/2305.11707/) (2023)
- [Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method](https://mlsanity.com/paper/2310.17918/) (2023)
- [KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection](https://mlsanity.com/paper/2310.09044/) (2023)
- [Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators](https://mlsanity.com/paper/2310.07289/) (2023)
- [Coherence boosting: When your pretrained language model is not paying enough attention](https://mlsanity.com/paper/2110.08294/) (2021)
- [Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check](https://mlsanity.com/paper/2403.18243/) (2024)
- [A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition](https://mlsanity.com/paper/2307.15252/) (2023)
- [Uncertainty Quantification via Stable Distribution Propagation](https://mlsanity.com/paper/2402.08324/) (2024)
- [Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback](https://mlsanity.com/paper/2309.17078/) (2023)
- [Hallucination Detection for Grounded Instruction Generation](https://mlsanity.com/paper/2310.15319/) (2023)
- [NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic](https://mlsanity.com/paper/2307.02849/) (2023)
- [Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding](https://mlsanity.com/paper/2310.13022/) (2023)
- [A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder](https://mlsanity.com/paper/2403.00891/) (2024)

## Built on by (most cited first, 8 of 8)

- [Large Language Models Hallucination: A Comprehensive Survey](https://mlsanity.com/paper/2510.06265/) (2025)
- [Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation](https://mlsanity.com/paper/2506.17088/) (2025)
- [Consensus Entropy: Harnessing Multi-VLM Agreement for Self-Verifying and Self-Improving OCR](https://mlsanity.com/paper/2504.11101/) (2025)
- [Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering](https://mlsanity.com/paper/2506.00491/) (2025)
- [Beyond Quality: Unlocking Diversity in Ad Headline Generation with Large Language Models](https://mlsanity.com/paper/2508.18739/) (2025)
- [Automated Creativity Evaluation of Language Models Across Open-Ended Tasks](https://mlsanity.com/paper/2606.11762/) (2026)
- [Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models](https://mlsanity.com/paper/2605.12138/) (2026)
- [Estimating the Black-box LLM Uncertainty with Distribution-Aligned Adversarial Distillation](https://mlsanity.com/paper/2605.05777/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are prone to hallucination with non-factual or unfaithful statements, which undermines the applications in real-world scenarios. Recent researches focus on uncertainty-based hallucination detection, which utilizes the output probability of LLMs for uncertainty calculation and does not rely on external knowledge or frequent sampling from LLMs. Whereas, most approaches merely consider the uncertainty of each independent token, while the intricate semantic relations among tokens and sentences are not well studied, which limits the detection of hallucination that spans over multiple tokens and sentences in the passage. In this paper, we propose a method to enhance uncertainty modeling with semantic graph for hallucination detection. Specifically, we first construct a semantic graph that well captures the relations among entity tokens and sentences. Then, we incorporate the relations between two entities for uncertainty propagation to enhance sentence-level hallucination detection. Given that hallucination occurs due to the conflict between sentences, we further present a graph-based uncertainty calibration method that integrates the contradiction probability of the sentence with its neighbors in the semantic graph for uncertainty calculation. Extensive experiments on two datasets show the great advantages of our proposed approach. In particular, we obtain substantial improvements with 19.78% in passage-level hallucination detection.