mlsanityData to 14 Oct 2026

Semantic Reformulation Entropy for Robust Hallucination Detection in QA Tasks

arXiv 2509.17445 ↗First posted 22 Sep 2025cs.CLcited by 4

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Abstract (arXiv)

Reliable question answering with large language models (LLMs) is challenged by hallucinations, fluent but factually incorrect outputs arising from epistemic uncertainty. Existing entropy-based semantic-level uncertainty estimation methods are limited by sampling noise and unstable clustering of variable-length answers. We propose Semantic Reformulation Entropy (SRE), which improves uncertainty estimation in two ways. First, input-side semantic reformulations produce faithful paraphrases, expand the estimation space, and reduce biases from superficial decoder tendencies. Second, progressive, energy-based hybrid clustering stabilizes semantic grouping. Experiments on SQuAD and TriviaQA show that SRE outperforms strong baselines, providing more robust and generalizable hallucination detection. These results demonstrate that combining input diversification with multi-signal clustering substantially enhances semantic-level uncertainty estimation.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 19 of its 20 references that are in the map. First posted on arXiv 22 Sep 2025.

Builds on

19 of 20 references.

References · 19

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  3. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  4. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  5. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  6. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
  7. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
  8. 2024Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMscited by 259
  9. 2015On Wasserstein Two Sample Testing and Related Families of Nonparametric Testscited by 166
  10. 2023Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answeringcited by 151
  11. 2021A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generationcited by 138
  12. 2024To Believe or Not to Believe Your LLMcited by 120
7 more
  1. 2023Enhancing Uncertainty-Based Hallucination Detection with Stronger Focuscited by 99
  2. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  3. 2024VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimationcited by 73
  4. 2023Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Familycited by 71
  5. 2024Improving Uncertainty Quantification in Large Language Models via Semantic Embeddingscited by 30
  6. 2025Semantic Energy: Detecting LLM Hallucination Beyond Entropycited by 18
  7. 2024Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Modelscited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026FaithSCAN: Model-Driven Single-Pass Hallucination Detection for Faithful Visual Question Answeringcited by 1
  2. 2026Lowest Span Confidence: Zero-Shot Hallucination Detection from a Single LLM Responsecited by 0
  3. 2026Knowledge Dependency Estimation for Reliable Question Answeringcited by 0
  4. 2026Diversity-Oriented Fine-Tuning for Uncertainty-Based Hallucination Detectioncited by 0