HalluLens: LLM Hallucination Benchmark
Open accessAbstract
Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang, Nicola Cancedda, Pascale Fung. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Bang et al. (2025) found that the model has sufficient knowledge to identify non-existing entities.
“Bang et al. (2025) had a similar observation by investigating the model's abstention behavior across mixed or completely fabricated entities, and found that the model has sufficient knowledge to identify non-existing entities.”
From HalluHard: A Hard Multi-Turn Hallucination Benchmark (2026) · cites this paper as Bang et al. (2025)
The paper’s discussion of place-frequency results (section headed “Lower values indicate better performance”; page unavailable) says models may have sufficient knowledge to identify non-existing entities associated with high-frequency places. It also says they are more uncertain about middle-frequency places. The NonExistentRefusal results (C.3.1–C.3.2; pages unavailable) show substantial variation across models and tasks, so the unqualified conclusion about identifying non-existing entities overstates the finding. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA clean test of in-parameter groundedness is whether models appropriately abstain when asked about non-existent entities or items.
“Beyond answerable questions, a clean test of in-parameter groundedness is whether models appropriately abstain when asked about non-existent entities or items (Bang et al., 2025; Kirichenko et al., 2025).”
From HalluHard: A Hard Multi-Turn Hallucination Benchmark (2026) · cites this paper as Bang et al., 2025
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 42
- Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774. · arXiv
- Alfonso Amayuelas, Kyle Wong, Liangming Pan, Wenhu Chen, and William Wang. 2023. Knowledge of knowledge: Exploring known-unknowns uncer- tainty with large language models. arXiv preprint arXiv:2305.13712. · arXiv
- Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, Tanmoy Chakraborty, Giovanni Luca Ciampaglia, David Corney, Renee DiResta, Emilio Ferrara, Scott Hale, Alon Halevy, et al. 2024. Factuality challenges in the era of large language models and opportuni- ties for fact-checking. Nature Machine Intelligence, 6(8):852-863.
- Forrest Bao, Miaoran Li, Rogger Luo, and Ofer Mendelevitch. 2024. HHEM-2.1-Open. · DOI
- Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. 2021. Extracting training data from large language models. In 30th USENIX Security Symposium (USENIX Security 21), pages 2633-2650.
- Qinyuan Cheng, Tianxiang Sun, Xiangyang Liu, Wen- wei Zhang, Zhangyue Yin, Shimin Li, Linyang Li, Zhengfu He, Kai Chen, and Xipeng Qiu. 2024. Can ai assistants know what they don't know? In Forty- first International Conference on Machine Learning.
- I Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, Pengfei Liu, et al. 2023. Factool: Factuality detec- tion in generative ai-a tool augmented framework for multi-task and multi-domain scenarios. arXiv preprint arXiv:2307.13528. · arXiv
- David Chiang and Peter Cholak. 2022. Overcoming a theoretical limitation of self-attention. In Proceed- ings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7654-7664.
- Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding, Vidhisha Balachandran, and Yulia Tsvetkov. 2024b. Don't hallucinate, abstain: Identifying llm knowl- edge gaps via multi-llm collaboration. ACL.
- Katja Filippova. 2020. Controlled hallucinations: Learning to generate faithfully from noisy data. In Findings of the Association for Computational Lin- guistics: EMNLP 2020, pages 864-870.
- Yuzhe Gu, Ziwei Ji, Wenwei Zhang, Chengqi Lyu, Dahua Lin, and Kai Chen. 2024. ANAH-v2: Scaling analytical hallucination annotation of large language models. In The Thirty-eighth Annual Conference on Neural Information Processing Systems.
- Michael Hahn. 2020. Theoretical limitations of self- attention in neural sequence models. Transactions of the Association for Computational Linguistics, 8:156- 171.
- Karl Moritz Hermann, Tomas Kocisky, Edward Grefen- stette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. Teaching machines to read and comprehend. Advances in neural information processing systems, 28.
- Xiangkun Hu, Dongyu Ru, Lin Qiu, Qipeng Guo, Tianhang Zhang, Yang Xu, Yun Luo, Pengfei Liu, Yue Zhang, and Zheng Zhang. 2024. Refchecker: Reference-based fine-grained hallucination checker and benchmark for large language models. arXiv preprint arXiv:2405.14486. · arXiv
- Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, et al. 2023. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232. · arXiv
- Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi- Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2023. Atlas: Few-shot learning with retrieval augmented language models. Journal of Machine Learning Research, 24(251):1-43.
- Alon Jacovi, Andrew Wang, Chris Alberti, Connie Tao, Jon Lipovetz, Kate Olszewska, Lukas Haas, Michelle Liu, Nate Keating, Adam Bloniarz, et al. 2025. The facts grounding leaderboard: Benchmarking llms' ability to ground responses to long-form input. arXiv preprint arXiv:2501.03200. · arXiv
- Ziwei Ji, Yuzhe Gu, Wenwei Zhang, Chengqi Lyu, Dahua Lin, and Kai Chen. 2024. ANAH: Analyt- ical annotation of hallucinations in large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: Long Papers), pages 8135-8158, Bangkok, Thailand. Association for Computational Linguistics. · DOI
- Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2023. Large language models struggle to learn long-tail knowledge. In In- ternational Conference on Machine Learning, pages 15696-15707.
- PMLR.
- Jungo Kasai, Keisuke Sakaguchi, yoichi takahashi, Ronan Le Bras, Akari Asai, Xinyan Velocity Yu, Dragomir Radev, Noah A. Smith, Yejin Choi, and Kentaro Inui. 2023. Realtime QA: What's the an- swer right now? In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track.
- Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, and Sanjiv Kumar. 2023a. Large language models with controllable working memory. In Findings of the As- sociation for Computational Linguistics: ACL 2023, pages 1774-1793.
- Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2023b. Halueval: A large- scale hallucination evaluation benchmark for large language models. In Proceedings of the 2023 Con- ference on Empirical Methods in Natural Language Processing, pages 6449-6464.
- Stephanie Lin, Jacob Hilton, and Owain Evans. 2022. Truthfulqa: Measuring how models mimic human falsehoods. In Proceedings of the 60th Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3214-3252.
- Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jian- meng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, et al. 2024. Mitigat- ing the alignment tax of rlhf. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 580-606.
- Tianyu Liu, Yizhe Zhang, Chris Brockett, Yi Mao, Zhi- fang Sui, Weizhu Chen, and William B Dolan. 2022. A token-level reference-free hallucination detection benchmark for free-form text generation. In Proceed- ings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6723-6737.
- Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023a. When not to trust language models: Investigating effectiveness of parametric and non-parametric mem- ories. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: Long Papers), pages 9802-9822.
- Jean-Baptiste Michel, Yuan Kui Shen, Aviva Presser Aiden, Adrian Veres, Matthew K Gray, Google Books Team, Joseph P Pickett, Dale Hoiberg, Dan Clancy, Peter Norvig, et al. 2011. Quantitative analysis of culture using millions of digitized books. science, 331(6014):176-182.
- Yifei Ming, Senthil Purushwalkam, Shrey Pandit, Zix- uan Ke, Xuan-Phi Nguyen, Caiming Xiong, and Shafiq Joty. 2024. Faitheval: Can your language model stay faithful to context, even if "the moon is made of marshmallows". arXiv.
- Jio Oh, Soyeon Kim, Junseok Seo, Jindong Wang, Ruochen Xu, Xing Xie, and Steven Euijong Whang. 2024. ERBench: An entity-relationship based au- tomatically verifiable hallucination benchmark for large language models. In The Thirty-eight Con- ference on Neural Information Processing Systems Datasets and Benchmarks Track.
- Yasumasa Onoe, Michael Zhang, Eunsol Choi, and Greg Durrett. 2022. Entity cloze by date: What lms know about unseen entities. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 693-702.
- YuHong Sun, Zhangyue Yin, Qipeng Guo, Jiawen Wu, Xipeng Qiu, and Hui Zhao. 2024. Benchmarking hal- lucination in large language models based on unan- swerable math word problem. In Proceedings of the 2024 Joint International Conference on Compu- tational Linguistics, Language Resources and Eval- uation (LREC-COLING 2024), pages 2178-2188, Torino, Italia. ELRA and ICCL.
- Kushal Tirumala, Daniel Simig, Armen Aghajanyan, and Ari Morcos. 2023. D4: Improving llm pretrain- ing via document de-duplication and diversification. Advances in Neural Information Processing Systems, 36:53983-53995.
- Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, pages 142- 147.
- Christian Tomani, Kamalika Chaudhuri, Ivan Evti- mov, Daniel Cremers, and Mark Ibrahim. 2024. Uncertainty-based abstention in llms improves safety and reduces hallucinations. arXiv preprint arXiv:2404.10960. · arXiv
- Hugo Touvron, Louis Martin, Kevin Stone, Peter Al- bert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, An- thony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Di- ana Liskovich, Yinghai Lu, Yuning Mao, Xavier Mar- tinet, Todor Mihaylov, Pushkar Mishra, Igor Moly- bog, Yixin Nie, Andrew Poulton, Jeremy Reizen- stein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subrama- nian, Xiaoqing Ellen Tan, Binh Tang, Ross Tay- lor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Ro- driguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023. Llama 2: Open foundation and fine- tuned chat models. Preprint, arXiv:2307.09288. · arXiv
- Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Xian- gru Tang, Tianhang Zhang, Cheng Jiayang, Yunzhi Yao, Wenyang Gao, Xuming Hu, Zehan Qi, et al. 2023. Survey on factuality in large language models: Knowledge, retrieval and domain-specificity. arXiv preprint arXiv:2310.07521. · arXiv
- Yuxia Wang, Minghan Wang, Muhammad Arslan Man- zoor, Fei Liu, Georgi Georgiev, Rocktim Das, and Preslav Nakov. 2024. Factuality of large language models: A survey. In Proceedings of the 2024 Con- ference on Empirical Methods in Natural Language Processing, pages 19519-19529.
- Jason Wei, Nguyen Karina, Hyung Won Chung, Yunxin Joy Jiao, Spencer Papay, Amelia Glaese, John Schulman, and William Fedus. 2024a. Measuring short-form factuality in large language models. arXiv preprint arXiv:2411.04368. · arXiv
- Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, et al. 2024b. Long-form factuality in large language models. arXiv preprint arXiv:2403.18802. · arXiv
- Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Zixia Hu, Jie Huang, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, and Quoc V Le. 2024c. Long-form factuality in large language mod- els. In The Thirty-eighth Annual Conference on Neu- ral Information Processing Systems.
- Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al. 2023. Siren's song in the ai ocean: a survey on hallucination in large language models. arXiv preprint arXiv:2309.01219. · arXiv
Cited by
Sorted by citations
AI-enabled scientific revolution in the age of generative AI: second NSF workshop report
Karpatne, A., Deshwal, A., Jia, X., Ding, W. Y., Steinbach, M. S., Zhang, A., & Kumar, V. (2025) · npj Artificial Intelligence · doi:10.1038/s44387-025-00018-6 · Cited by 17 · see citing works
The Risks of AI-Generated, Hyper-Personalized Digital Advertisements
LeBrun, A. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00935-z · Cited by 3 · see citing works
FAITH: A Framework for Assessing Intrinsic Tabular Hallucinations in Finance
Zhang, M., Fu, J., Warrier, T., Wang, Y., Tan, T., & Huang, K. (2025) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3768292.3770433 · Cited by 7 · see citing works
A Survey of Hallucination in Large Language Models
何, 金. (2026) · Artificial Intelligence and Robotics Research · doi:10.12677/airr.2026.151016 · Cited by 3 · see citing works
Pai, K.-C., & Hsu, W.-C. (2026) · Computer Standards & Interfaces · doi:10.1016/j.csi.2026.104163 · Cited by 3 · see citing works
Safety and security of large language models in healthcare
Clusmann, J., Freyer, O., Ostermann, M., Ferber, D., Laleh, N. G., Hilgers, L., Kolbinger, F. R., Schneider, C. V., Downing, A., Wekenborg, M., et al. (2026) · Nature · doi:10.1038/s41586-026-10687-1 · Cited by 4 · see citing works
Xia, S.-J., Zhang, H., Jiang, Y., Chen, X., Chen, Y., Li, Z., Wan, Z., & Sun, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5731427 · Cited by 3 · see citing works
Big Loop and Atomization: A Holistic Review on the Expansion Capabilities of Large Language Models
Hu, Z., Huang, Y., Feng, J., & Deng, C. (2025) · Applied Sciences · doi:10.3390/app15179466 · Cited by 2 · see citing works
Generative AI’s Impact on Individuals and Organizations: An Analysis Based on Human-AI Collaboration
Latto, C., Leyer, M., Khac, L. T. D., Mezger, M. F., Tate, M., & Richter, A. (2026) · Group Decision and Negotiation · doi:10.1007/s10726-026-10007-y · Cited by 3 · see citing works
MHB: Medical Hallucination Benchmark for Large Language Models in Complex Clinical Tasks
Lu, J., Liu, J., Zheng, X., Yang, M., Wang, J., Wang, P., & Zhang, Y. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i45.41243 · Cited by 1 · see citing works
Hallucination-Free Automatic Question & Answer Generation for Intuitive Learning
Wang, N., & Katsaggelos, A. K. (2025) · IEEE International Conference on Image Processing Workshops (ICIPW) · doi:10.1109/icipw68931.2025.11386040 · Cited by 1 · see citing works
A White-Box Benchmark for Intrinsic Hallucination Detection in Turkish LLMs
Bayramoglu, B., Ficici, M., Bildirici, F., Kızıltepe, R. S., & Karakuş, M. (2026) · International Informatics and Software Engineering Conference (IISEC) · doi:10.1109/iisec69317.2026.11418463 · Cited by 1 · see citing works
Yajnik, A., & Pushkala, S. A. (2026) · IEEE Access · doi:10.1109/access.2026.3719291 · Cited by 1 · see citing works
HalluHard: A Hard Multi-Turn Hallucination Benchmark
Fan, D., Delsad, S., Flammarion, N., & Andriushchenko, M. (2026) · arXiv · doi:10.48550/arxiv.2602.01031
SHAPE: Symptom-based Hallucination-driven Augmented Prompt Engineering (Preprint)Preprint
Kim, Y.-J., Shin, J., You, H.-S., Yoon, S., An, D.-J., Lee, A.-Y., Yoo, I.-S., Jeon, H. J., Lee, J. W., & Kim, K. D. (2025) · doi:10.2196/preprints.86028
Ghazal, M. A., Salman, H., Yaghi, M., Gad, A., Hamdan, H., Al-Jamri, D., Ahmed, S., & Yousaf, J. (2025) · International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud66139.2025.00060
Hallucination-Free Causal Graph-Guided AI Framework for Intuitive Question and Answer Generation
Wang, N., & Katsaggelos, A. K. (2026) · International Journal of Multimedia Data Engineering and Management · doi:10.4018/ijmdem.404003
No skin in the game: why agentic AI requires principal-agent governance
Prause, M. (2026) · AI and Ethics · doi:10.1007/s43681-026-01067-6
Slurry-as-a-Service: A Modest Proposal on Scalable Pluralistic Alignment for Nutrient Optimization
Hong, R. S., Eiger, Y., Hutson, J. A., Keyes, O., & Agnew, W. S. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798866
Context Definition for Large Language Model Validation and Enhancement
Feng, B., & Ostrowski, D. A. (2026) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc67292.2026.00058
Gorji, A., Shahverdi, H., Saberi, A., Gheiji, B., Farahani, S., Azemi, G., & Ieva, A. D. (2026) · medRxiv · doi:10.64898/2026.05.01.26349658
Adnan, M., Ali, K., Shamim, M., & Sarwar, N. (2026) · International Conference on Emerging Computational Intelligence (ICECI) · doi:10.1109/iceci69159.2026.11519426
Khan, A., Khan, S., Khan, M. S. R., Su’ud, M. M., Alam, M. M., & Mallik, S. (2026) · Research Square · doi:10.21203/rs.3.rs-9707462/v1
Promises and Risks in using LLMs in Scientific Review
Kuladinithi, K., Fuger, K., Makama, A., Timm‐Giel, A., Kiener, M., & Bozenhard, J. (2026) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops68308.2026.11585353
Human-in-the-Loop Framework for Hallucination Mitigation: A Controlled Comparison of GPT and LLaMA
Kai, J. N. J., Syahidi, A. A., & Kiong, K. B. (2026) · International Conference on Business and Industrial Research (ICBIR) · doi:10.1109/icbir69932.2026.11584794
Project Planning in Construction 4.0: AI-Driven Chatbots for Project Planning Challenges
Kobašević, E., & Galić, M. (2026) · Slovak Journal of Civil Engineering · doi:10.2478/sjce-2026-0010
A Multi-agent Framework for Factuality Hallucination Detection Using Complex Knowledge Graph
Minh, L. X., Khuong, H. N. D., Minh, N. H., Nhut, N. M., Phat, T. L., Vinh, N. N., An, T. C., Tuyen, B. T. T., Quan, T. T., & Hua, T. P. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2480-7_10
Large Language Model-Based Data Querying and Analysis for Manufacturing Industry
Radhakrishnan, R., Beden, S., Shahid, Z., Giannetti, C., & Beckmann, A. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2864-5_29
Basnet, S., & Chang, K. N. (2026) · Transportation Research Today · doi:10.1016/j.trt.2026.100012
From Human to Hybrid: Artificial Intelligence and the Transformation of Language Services
Tavares, C., de Oliveira, L. G., & Neves, R. (2026) · Societies · doi:10.3390/soc16080236
Activation-based efficient routing for retrieval-augmented generation in LLMs
Ren, S. (2026) · Intelligent Data Analysis · doi:10.1177/1088467x261471261
Análise de Falhas na Detecção de Alucinação em Textos Jurídico-Policiais em Português
Barcelar, R. R., & Ventura, T. M. (2026) · doi:10.5753/pesquisanuvem.2026.23945
Bandhey, H., Lipschutz-Villa, G., Dinh, K., Kamoun, M., & Urbanowicz, R. J. (2026) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3795101.3814722
From Output Errors to Workflow Harm: A Practitioner-Audit Method for LLM-Mediated ResearchPreprint
Austria, D., McCollister, B., Lindsey, J. E., Arowolo, M. O., & Okon, M. (2026) · medRxiv · doi:10.64898/2026.08.13.26360414 · Cited by 1 · see citing works
Personal Data Accuracy is a Blind Spot in LLM Privacy Research - Opinion Paper
Magnussen, K., & Böhme, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-35899-8_2
Torres, A., & Fernandez, M. (2024) · Journal of Health Informatics and Digital Systems · doi:10.68159/z595811548
A Survey of Evaluation Methods for the Security and Trustworthiness of Large Language Models
Zhao, L., Xu, Z., Zhao, R., Li, C., Yang, B., & Feng, D. (2026) · International Conference on Machine Learning Technologies (ICMLT) · doi:10.1109/icmlt69916.2026.11689272
A Knowledge‐Augmented Wearable System for Interpretable Plant Health Monitoring
Dong, X., Luo, Y., Qin, J., Ye, J., Fan, Y., Pu, M., Zong, Z., Xu, G., Ding, H., & Wu, Z. (2026) · FlexTech · doi:10.1002/fle2.70020
AI-in-the-human-loop: an operational model for mature human-AI collaboration
Casadei, R., Delnevo, G., Bassi, B., Ceccarini, C., & Mirri, S. (2026) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-026-05135-x
Tool-grounded large language models for decision-level fusion in fault diagnosis
Hasan, A. (2026) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2026.115025
Multidisciplinary Hallucination Detection and Assessment Tool for LLMs
Kösedağ, E. A., Ehlibeyt, S. T., Yılmaz, A. A., & Baykara, M. (2026) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap71869.2026.11727928
Büyük Dil Modellerinin Türkçe Sözcük Bilgisi: Halüsinasyon ve Çekimserlik
Göz, F. (2026) · Journal of Intelligent Systems Theory and Applications · doi:10.38016/jista.2027216
- Authors
- Bang, Y., Ji, Z., Schelten, A., Hartshorn, A., Fowler, T., Zhang, C., Cancedda, N., & Fung, P.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Venue
- Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2025
- Pages
- 24128-24156
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4412945166
- Cited by
- 41
- References
- 0
- Citations by year
- 2026: 32 · 2025: 8 · 2024: 1
- FWCI
- 38.13
- 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)
- 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
- large language model hallucination, hallucination benchmark, hallucination detection, 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 ›
Are you an author, or do you see a problem with this page? Request a review
