Papers with sentence-level hallucination detection
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (2023.emnlp-main)
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| Challenge: | Existing fact-checking approaches require access to external databases or external databases . a lack of external databases can undermine trust in large language models. |
| Approach: | They propose a sampling-based approach to fact-check black-box models without external databases. |
| Outcome: | The proposed approach can be used to fact-check black-box models without external databases . it can detect non-factual and factual sentences and rank passages in terms of factuality . |
ContextCheck: Sentence-Level Faithfulness Verification with Context-Aware Disambiguation (2026.findings-acl)
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Yueqin Yin, Yaxi Li, Xin Liu, Xun Wang, Kaiqiang Song, Simin Ma, Shujian Liu, Sathish Reddy Indurthi, Haoyun Deng, Pengcheng He, Mingyuan Zhou, Song Wang
| Challenge: | Large language models often hallucinate, producing content that is factually incorrect or not grounded in the sources. |
| Approach: | They propose a framework for sentence-level faithfulness verification with context-aware disambiguation. |
| Outcome: | The proposed framework improves Macro F1 by over 10 points compared to baselines on three context-dependent datasets. |