A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation
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Tianyu Liu, Yizhe Zhang, Chris Brockett, Yi Mao, Zhifang Sui, Weizhu Chen, Bill Dolan. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.
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Liu et al. (2022) introduce a hallucination detection dataset, but texts are obtained by perturbing factual texts and thus may not reflect true LLM hallucination.
“Further, Liu et al. (2022) introduce a hallucination detection dataset, however, texts are obtained by perturbing factual texts and thus may not reflect true LLM hallucination.”
From SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (2023) · cites this paper as Liu et al., 2022
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Investigating the Intervention in Parallel Conversations
Mochizuki, S., Yamashita, S., Kawasaki, K., Yuasa, R., Kubota, T., Ogawa, K., Baba, J., & Higashinaka, R. (2023) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3623809.3623863 · Cited by 5 · see citing works
Histoire et perspectives de l’intelligence artificielle
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Zhang, C., Zhu, S., Yang, X., Tseng, Y.-C., Jiang, S., & Rzeszotarski, J. M. (2025) · Proceedings of the 7th ACM Conference on Conversational User Interfaces · doi:10.1145/3719160.3736618 · Cited by 4 · see citing works
Multimodal Coherent Explanation Generation of Robot Failures
Pramanick, P., & Rossi, S. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802671 · Cited by 4 · see citing works
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Incorporating RAG for Factual Hallucination Detection Modeling in Intelligent Educational system
Zheng, L., Ye, J., Zhao, G. L., Luo, S., Nan, M., & Xie, Y. (2025) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste64638.2025.11092000 · Cited by 4 · see citing works
Human-inspired Perspectives: A Survey on AI Long-term Memory
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“According to Zhang et al. [145], hallucinations refer to the phenomenon where the content retrieved and generated through parametric memory does not align with the source input provided by the user [284], previously generated content [285], or established facts [286].”
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Learning to Generate Code Sketches.Preprint
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Identifying large language model hallucinations in health communication
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Meta-requirements for LLM-Based Knowledge Exploration Tools in Information Systems Research
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Exploring Accurate and Generic Simile Knowledge from Pre-trained Language Models
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Semantic Reformulation Entropy for Robust Hallucination Detection in QA Tasks
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Estimating Hallucination Risk in Large Language Models with Intrinsic Priors and Entailment Signals
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Small Models, Big Variances: Unveiling the Impact of Execution Environments on SLMs’ Security
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Multimodal AI for Healthcare: From Medical Imaging to Omics and Vision–Language Models
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Self-citation
- Authors
- Liu, T., Zhang, Y., Brockett, C., Mao, Y., Sui, Z., Chen, W., & Dolan, B.
- Affiliations
- Peking University (CN)
- Meta (United States) (US)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2022
- Pages
- 6723-6737
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3155332104
- Cited by
- 63
- References
- 61
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 15 · 2023: 13 · 2022: 3 · 2021: 3
- FWCI
- 7.05
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (U19A2065)
- National Key Research and Development Program of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- hallucination detection, reference-free evaluation, large language models, benchmark design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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