Elamur Research⌕
☰
home / papers / 2026 / PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations

PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations

Paper · 2026 · Yuhe Wu, Guangyu Wang, Yuran Chen et al.

Summary

Most LLM hallucination benchmarks, such as TruthfulQA, HaluEval and FreshQA, mix query types and score only the final answer. They show how often a model fails but not at which generation stage. PRISM treats hallucination evaluation as diagnosis. It splits failures into four dimensions tied to memory, reasoning and instruction handling: Knowledge Error (KE), Knowledge Missing (KM), Reasoning Error (RE) and Instruction Following Error (IFE).

Each subset is designed to probe a single failure mode in isolation. Sources include:

  • long-tail Wikipedia and Baidu Baike entries;
  • CNN, Reuters and arXiv texts from March–November 2025, after most models' training cutoffs;
  • invented entities and private-domain data;
  • self-contained problems such as IMO tasks and code generation;
  • template-generated instructions with stacked constraints.

Items are built by a multi-agent pipeline and then curated by experts. The authors tested 24 open-source and proprietary LLMs. Proprietary models generally score higher, but every model trades strength in one dimension for weakness in another. Mitigation methods such as reasoning SFT, instruct tuning and ICL help some dimensions while hurting others. This argues for targeted rather than blanket hallucination fixes.

Key points

  • PRISM has 9,448 instances across 65 sub-tasks: RE 2,995 (31.7%), IFE 2,442 (25.9%), KM 2,078 (22.0%), KE 1,933 (20.5%).
  • Four agents build the data: a schema normalizer, an evidence retriever, a type classifier, and a quality scorer that assigns A/B/C grades. Domain experts then select items for clarity, relevance and coherence.
  • Attention maps show two distinct failure patterns:
    • In KE, attention is diffuse and parametric priors override the evidence in the input.
    • In KM, attention fixates on misleading context tokens; the paper gives a Qwen3-4B case.
  • In IFE tasks, refusing costs more compute than producing a hallucinated answer. This holds for Qwen3-1.7B, Qwen3-4B, Qwen3-14B and Llama3.1-8B, with more active neurons, more GFLOPs and lower attention sparsity. For example, Qwen3-14B uses 802.6 GFLOPs when refusing versus 755.0 when not.
  • Limitations: text only, five high-resource languages, and possible contamination for models with undisclosed training cutoffs. Project site: acl-prism.cc.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Chinese University of Hong KongHong Kong · China159 works · 16,154 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3,894 worksReasoning · 1,685 worksInterpretability · 149 works

Cite

Cite as Yuhe Wu, Guangyu Wang, Yuran Chen, Jiatong Zhang, Yutong Zhang, Yujie Chen, Jiaming Shang, Guang Zhang and Zhuang Liu. 2026. PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1551

@inproceedings{wu2026prism,
  title = {{PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations}},
  author = {Yuhe Wu, Guangyu Wang, Yuran Chen, Jiatong Zhang, Yutong Zhang, Yujie Chen, Jiaming Shang, Guang Zhang and Zhuang Liu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1551},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1551}
}

References

126 references, 61 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Samir Abdaljalil, Hasan Kurban, and Erchin Serpedin. 2025. HalluVerse25: Fine-grained Multilingual Benchmark Dataset for LLM Hallucinations. arXiv preprint arXiv:2503.07833. — [arXiv:2503.07833](https://arxiv.org/abs/2503.07833) find
  2. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2024. GPT-4 Technical Report. arXiv preprint arXiv:2303.08774.
  3. AIMeta. 2025. The Llama4 Herd: The Beginning of a New Era of Natively Multimodal AI Innovation. find
  4. Aisha Alansari and Hamzah Luqman. 2026. Large Language Models Hallucination: A Comprehensive Survey. arXiv preprint arXiv:2510.06265. — [arXiv:2510.06265](https://arxiv.org/abs/2510.06265) find
  5. Dang Anh-Hoang, Vu Tran, and Le-Minh Nguyen. 2025. Survey and analysis of hallucinations in large language models: attribution to prompting strategies or model behavior. Frontiers in Artificial Intelligence, Volume 8 - 2025. find
  6. Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track), pages 228–238. find
  7. Robert Baldock, Hartmut Maennel, and Behnam Neyshabur. 2021. Deep Learning Through the Lens of Example Difficulty. In Advances in Neural Information Processing Systems, volume 34, pages 10876– 10889. find
  8. Deborah L Bandalos. 2018. Measurement theory and applications for the social sciences. Guilford Publications. find
  9. Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang, Nicola Cancedda, and Pascale Fung. 2025. HalluLens: LLM Hallucination Benchmark. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 24128–24156.
  10. Forrest Sheng Bao, Miaoran Li, Renyi Qu, Ge Luo, Erana Wan, Yujia Tang, Weisi Fan, Manveer Singh Tamber, Suleman Kazi, Vivek Sourabh, Mike Qi, Ruixuan Tu, Chenyu Xu, Matthew Gonzales, Ofer Mendelevitch, and Amin Ahmad. 2025. FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern L… find
  11. Adrien Barbaresi. 2021. Trafilatura: A Web Scraping Library and Command-Line Tool for Text Discovery and Extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstra…
  12. Bernd Bohnet, Vinh Q. Tran, Pat Verga, Roee Aharoni, Daniel Andor, Livio Baldini Soares, Massimiliano Ciaramita, Jacob Eisenstein, Kuzman Ganchev, Jonathan Herzig, Kai Hui, Tom Kwiatkowski, Ji Ma, Jianmo Ni, Lierni Sestorain Saralegui, Tal Schuster, William W. Cohen, Michael Collins, Dipanjan Das, a… — Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
  13. Daniil A Boiko, Robert MacKnight, Gabe Gomes, and Ben Kline. 2023. Autonomous chemical research with large language models. Nature, 624(7992):570– 578.
  14. Anthropic. 2025. Claude3.7 Sonnet and Claude Code. find
  15. Andres M Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller. 2024. Augmenting large language models with chemistry tools. Nature Machine Intelligence, 6(5):525–535. find
  16. Orlando Ayala and Patrice Bechard. 2024. Reducing hallucination in structured outputs via Retrieval-Augmented Generation. In Proceedings of the 2024 Conference of the North American Chapter of the Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelak… find
  17. Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005. Learning to Rank using Gradient Descent. In Proceedings of the 22nd international conference on Machine learning, pages 89–96.
  18. Neeloy Chakraborty, Melkior Ornik, and Katherine Driggs-Campbell. 2025. Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art. ACM Comput. Surv., 57(7). find
  19. Kedi Chen, Qin Chen, Jie Zhou, He Yishen, and Liang He. 2024a. DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9057–9079. find
  20. Shiqi Chen, Yiran Zhao, Jinghan Zhang, I-Chun Chern, Siyang Gao, Pengfei Liu, and Junxian He. 2023. FELM: benchmarking factuality evaluation of large language models. In Proceedings of the 37th International Conference on Neural Information Processing Systems, pages 44502–44523.
  21. D. Diakoulaki, G. Mavrotas, and L. Papayannakis. 1995. Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7):763–770. find
  22. Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang (Lorraine) Li, Liwei Jiang, Bill Yuchen Lin, Sean Welleck, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena Hwang, Soumya Sanyal, Xiang Ren, Allyson Ettinger, Zaid Harchaoui, and Yejin Choi. 2023. Faith and Fate: Limits of Transformers on Compositiona…
  23. Deanna Emery, Michael Goitia, Freddie Vargus, and Iulia Neagu. 2025. HalluMix: A Task-Agnostic, Multi-Domain Benchmark for Real-World Hallucination Detection. arXiv preprint arXiv:2505.00506. — [arXiv:2505.00506](https://arxiv.org/abs/2505.00506) find
  24. Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, and 1 others. 2020. The Pile: An 800GB Dataset of Diverse Text for Language Modeling. arXiv preprint arXiv:2101.00027. find
  25. Google DeepMind. 2026. Gemini 3.1 pro - model card. find
  26. Xiang Chen, Duanzheng Song, Honghao Gui, Chenxi Wang, Ningyu Zhang, Yong Jiang, Fei Huang, Chengfei Lyu, Dan Zhang, and Huajun Chen. 2024b. FactCHD: Benchmarking Fact-Conflicting Hallucination Detection. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJ… find
  27. Neel Guha, Julian Nyarko, Daniel Ho, Christopher Ré, Adam Chilton, Aditya K, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel Rockmore, Diego Zambrano, Dmitry Talisman, Enam Hoque, Faiz Surani, Frank Fagan, Galit Sarfaty, Gregory Dickinson, Haggai Porat, Jason Hegland, and 21 others. 2023. L… — LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
  28. Qinyuan Cheng, Tianxiang Sun, Wenwei Zhang, Siyin Wang, Xiangyang Liu, Mozhi Zhang, Junliang He, Mianqiu Huang, Zhangyue Yin, Kai Chen, and Xipeng Qiu. 2023. Evaluating Hallucinations in Chinese Large Language Models. arXiv preprint arXiv:2310.03368. — [arXiv:2310.03368](https://arxiv.org/abs/2310.03368) find
  29. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. Nature, 645(8081):633–638.
  30. Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, and 1 others. 2025. Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities. arX…
  31. Stephen N Haynes, David Richard, and Edward S Kubany. 1995. Content Validity in Psychological Assessment: A Functional Approach to Concepts and Methods. Psychological assessment, 7(3):238. find
  32. Jiaxi Cui, Munan Ning, Zongjian Li, Bohua Chen, Yang Yan, Hao Li, Bin Ling, Yonghong Tian, and Li Yuan. 2024. Chatlaw: A Multi-Agent Collaborative Legal Assistant with Knowledge Graph Enhanced Mixture-of-Experts Large Language Model. arXiv preprint arXiv:2306.16092.
  33. Yancheng He, Shilong Li, Jiaheng Liu, Yingshui Tan, Weixun Wang, Hui Huang, Xingyuan Bu, Hangyu Guo, Chengwei Hu, Boren Zheng, Zhuoran Lin, Dekai Sun, Zhicheng Zheng, Wenbo Su, and Bo Zheng. 2025. Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models. In Proceedings of the 63rd…
  34. Matthew Dahl, Varun Magesh, Mirac Suzgun, and Daniel E Ho. 2024. Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models. Journal of Legal Analysis, 16(1):64–93.
  35. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021. Measuring Massive Multitask Language Understanding. In 9th International Conference on Learning Representations, ICLR 2021,Virtual Event, Austria, May 3-7, 2021.
  36. Alex Heyman and Joel Zylberberg. 2025. Reasoning Large Language Model Errors Arise from Hallucinating Critical Problem Features. arXiv preprint arXiv:2505.12151. — [arXiv:2505.12151](https://arxiv.org/abs/2505.12151) find
  37. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020. The Curious Case of Neural Text Degeneration. In International Conference on Learning Representations.
  38. 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:2405.14486](https://arxiv.org/abs/2405.14486) find
  39. Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2025. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions. ACM Transactions on Information Systems,…
  40. Ziwei Ji, Yuzhe Gu, Wenwei Zhang, Chengqi Lyu, Dahua Lin, and Kai Chen. 2024. ANAH: Analytical Annotation of Hallucinations in Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8135–8158. find
  41. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023a. Survey of Hallucination in Natural Language Generation. ACM Computing Surveys, 55(12):1–38.
  42. Ziwei Ji, Tiezheng Yu, Yan Xu, Nayeon Lee, Etsuko Ishii, and Pascale Fung. 2023b. Towards Mitigating LLM Hallucination via Self Reflection. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1827–1843.
  43. Nan Jiang, Qi Li, Lin Tan, and Tianyi Zhang. 2024a. Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Code. arXiv preprint arXiv:2410.09997. — [arXiv:2410.09997](https://arxiv.org/abs/2410.09997) find
  44. Yuxin Jiang, Yufei Wang, Xingshan Zeng, Wanjun Zhong, Liangyou Li, Fei Mi, Lifeng Shang, Xin Jiang, Qun Liu, and Wei Wang. 2024b. FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Comput…
  45. Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2023. Large Language Models Struggle to Learn Long-Tail Knowledge. In International conference on machine learning, pages 15696–15707. PMLR.
  46. Michael Kane. 2013. The Argument-Based Approach to Validation. School Psychology Review, 42(4):448– 457. find
  47. Jungo Kasai, Keisuke Sakaguchi, yoichi takahashi, Ronan Le Bras, Akari Asai, Xinyan Yu, Dragomir Radev, Noah A Smith, Yejin Choi, and Kentaro Inui. 2023. RealTime QA: What’s the Answer Right Now? In Advances in Neural Information Processing Systems, volume 36, pages 49025–49043.
  48. Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal. 2023. Decomposed Prompting: A Modular Approach for Solving Complex Tasks. In The Eleventh International Conference on Learning Representations.
  49. Emre Kiciman, Robert Ness, Amit Sharma, and Chenhao Tan. 2024. Causal Reasoning and Large Language Models: Opening a New Frontier for Causality. Transactions on Machine Learning Research. find
  50. Julia Kreutzer, Isaac Caswell, Lisa Wang, Ahsan Wahab, Daan van Esch, Nasanbayar Ulzii-Orshikh, Allahsera Tapo, Nishant Subramani, Artem Sokolov, Claytone Sikasote, Monang Setyawan, Supheakmungkol Sarin, Sokhar Samb, Benoît Sagot, Clara Rivera, Annette Rios, Isabel Papadimitriou, Salomey Osei, Pedro… — Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
  51. Yongchan Kwon, Shang Zhu, Federico Bianchi, Kaitlyn Zhou, and James Zou. 2025. ReasonIF: Large Reasoning Models Fail to Follow Instructions During Reasoning. arXiv preprint arXiv:2510.15211. find
  52. Tamera Lanham, Anna Chen, Ansh Radhakrishnan, Benoit Steiner, Carson Denison, Danny Hernandez, Dustin Li, Esin Durmus, Evan Hubinger, Jackson Kernion, and 1 others. 2023. Measuring Faithfulness in Chain-of-Thought Reasoning. arXiv preprint arXiv:2307.13702.
  53. Hugo Laurençon, Lucile Saulnier, Thomas Wang, Christopher Akiki, Albert Villanova del Moral, Teven Le Scao, Leandro Von Werra, Chenghao Mou, Eduardo González Ponferrada, Huu Nguyen, Jörg Frohberg, Mario Šaško, Quentin Lhoest, Angelina McMillan-Major, Gerard Dupont, Stella Biderman, Anna Rogers, Loub… — The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
  54. Ernests Lavrinovics, Russa Biswas, Katja Hose, and Johannes Bjerva. 2025. MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations. arXiv preprint arXiv:2505.14101. find
  55. Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2023. HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6449–6464.
  56. Yihan Li, Xiyuan Fu, Ghanshyam Verma, Paul Buitelaar, and Mingming Liu. 2025. Mitigating Hallucination in Large Language Models (LLMs): An Application-Oriented Survey on RAG, Reasoning, and Agentic Systems. arXiv preprint arXiv:2510.24476. — [arXiv:2510.24476](https://arxiv.org/abs/2510.24476) find
  57. Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Yezhaohui Wang, Dawei He, Cheng Peng, Zhonghao Wang, and Haiying Deng. 2024a. UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation. In Proceedings of the 62nd Annual Meeting of the As… find
  58. Xun Liang, Shichao Song, Zifan Zheng, Hanyu Wang, Qingchen Yu, Xunkai Li, Rong-Hua Li, Yi Wang, Zhonghao Wang, Feiyu Xiong, and Zhiyu Li. 2024b. Internal Consistency and Self-Feedback in Large Language Models: A Survey. arXiv preprint arXiv:2407.14507. — [arXiv:2407.14507](https://arxiv.org/abs/2407.14507)
  59. Qian Lin, Junyi Li, and Hwee Tou Ng. 2025. DynaQuest: A Dynamic Question Answering Dataset Reflecting Real-World Knowledge Updates. In Findings of the Association for Computational Linguistics: ACL 2025, pages 26918–26936. find
  60. Zhuang Liu, Shiyao Qian, Shuirong Cao, and Tianyu Shi. 2025. Mitigating Age-related Bias in Large Language Models: Strategies for Responsible Artificial Intelligence Development. INFORMS Journal on Computing, 2(1):1–22. find
  61. Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (V… find
  62. Jacob Menick, Maja Trebacz, Vladimir Mikulik, John Aslanides, Francis Song, Martin Chadwick, Mia Glaese, Susannah Young, Lucy Campbell-Gillingham, Geoffrey Irving, and Nat McAleese. 2022. Teaching language models to support answers with verified quotes. arXiv preprint arXiv:2203.11147.
  63. Samuel Messick. 1994. VALIDITY OF PSYCHO-LOGICAL ASSESSMENT: VALIDATION OF IN-FERENCES FROM PERSONS’ RESPONSES AND PERFORMANCES AS SCIENTIFIC INQUIRY INTO SCORE MEANING. ETS Research Report Series, 1994(2):i–28. find
  64. Stephanie Lin, Jacob Hilton, and Owain Evans. 2022. TruthfulQA: Measuring How Models Mimic Human Falsehoods. In Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers), pages 3214–3252.
  65. Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023. FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural L…
  66. Yen-Ting Lin and Yun-Nung Chen. 2023. LLM-EVAL: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models. In Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023).
  67. Abhika Mishra, Akari Asai, Vidhisha Balachandran, Yizhong Wang, Graham Neubig, Yulia Tsvetkov, and Hannaneh Hajishirzi. 2024. Fine-grained Hallucination Detection and Editing for Language Models. arXiv preprint arXiv:2401.06855.
  68. MingShan Liu and Jialing Fang. 2025. Enhancing Mathematical Reasoning in Large Language Models with Self-Consistency-Based Hallucination Detection. arXiv preprint arXiv:2504.09440. find
  69. Cheng Niu, Yuanhao Wu, Juno Zhu, Siliang Xu, KaShun Shum, Randy Zhong, Juntong Song, and Tong Zhang. 2024. RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volu…
  70. Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024. Lost in the Middle: How Language Models Use Long Contexts. Transactions of the Association for Computational Linguistics, 12:157–173.
  71. Ye Liu, Kazuma Hashimoto, Yingbo Zhou, Semih Yavuz, Caiming Xiong, and Philip S. Yu. 2021. Dense Hierarchical Retrieval for Open-Domain Question Answering. In Findings of the Association for Computational Linguistics: EMNLP 2021.
  72. Zhuang Liu, Degen Huang, Kaiyu Huang, Zhuang Li, and Jun Zhao. 2020. FinBERT: A Pre-trained Financial Language Representation Model for Financial Text Mining. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pages 4513–4519. ijcai.org. find
  73. Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Yuanhong Zheng, Dongsheng Ma, and 42 others. 2025. MinerU2.5: A Decoupled Vision-Language M…
  74. Jio Oh, Soyeon Kim, Junseok Seo, Jindong Wang, Ruochen Xu, Xing Xie, and Steven Euijong Whang. 2024. ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models. In Advances in Neural Information Processing Systems, volume 37, pages 53064–53101. find
  75. OpenAI. 2024. Hello gpt-4o. find
  76. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. J. Mach. Learn. Res., 21(1).
  77. OpenAI. 2025. Responses API Reference. https://platform.openai.com/docs/api-reference/responses/create. Accessed 2025-12-26. ↗
  78. A B M Ashikur Rahman, Saeed Anwar, Muhammad Usman, and Ajmal Mian. 2024. DefAn: Definitive Answer Dataset for LLMs Hallucination Evaluation. arXiv preprint arXiv:2406.09155. — [arXiv:2406.09155](https://arxiv.org/abs/2406.09155) find
  79. Jie Ouyang, Tingyue Pan, Mingyue Cheng, Ruiran Yan, Yucong Luo, Jiaying Lin, and Qi Liu. 2025. HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Vol… find
  80. Abhilasha Ravichander, Shrusti Ghela, David Wadden, and Yejin Choi. 2025. HALoGEN: Fantastic LLM Hallucinations and Where to Find Them. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1402–1425.
  81. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. 2022. Tra… — Training language models to follow instructions with human feedback
  82. Lawrence A Palinkas, Sarah M Horwitz, Carla A Green, Jennifer P Wisdom, Naihua Duan, and Kimberly Hoagwood. 2015. Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research. Administration and policy in mental health and mental health services research,… find
  83. Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2024. Unifying large language models and knowledge graphs: A roadmap. IEEE Transactions on Knowledge and Data Engineering, 36(7):3580–3599.
  84. Shrey Pandit, Jiawei Xu, Junyuan Hong, Zhangyang Wang, Tianlong Chen, Kaidi Xu, and Ying Ding. 2025. MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 285… find
  85. Bibek Paudel, Alexander Lyzhov, Preetam Joshi, and Puneet Anand. 2025. HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification. arXiv preprint arXiv:2504.07069. — [arXiv:2504.07069](https://arxiv.org/abs/2504.07069) find
  86. Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. 2023. Instruction Tuning with GPT-4. arXiv preprint arXiv:2304.03277.
  87. Benjamin Plaut, Nguyen X. Khanh, and Tu Trinh. 2025. Probabilities of Chat LLMs Are Miscalibrated but Still Predict Correctness on Multiple-Choice Q&A. Transactions on Machine Learning Research. find
  88. Pranab Sahoo, Prabhash Meharia, Akash Ghosh, Sriparna Saha, Vinija Jain, and Aman Chadha. 2024. A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 11709–11724. find
  89. Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, Perry Payne, Martin Seneviratne, Paul Gamble, Chris Kelly, Abubakr Babiker, Nathanael Schärli, Aakanksha Chowdhery, Philip Mansfield, Dina Demner-Fushma… — Large Language Models Encode Clinical Knowledge
  90. Samuel J Stratton. 2024. Purposeful Sampling: Advantages and Pitfalls. Prehospital and Disaster Medicine, 39(2):121–122. find
  91. Atsushi Suzuki, Yulan He, Feng Tian, and Zhongyuan Wang. 2025. Hallucinations are inevitable but can be made statistically negligible. the "innate" inevitability of hallucinations cannot explain practical llm issues. arXiv preprint arXiv:2502.12187. — [arXiv:2502.12187](https://arxiv.org/abs/2502.12187) find
  92. Liyan Tang, Igor Shalyminov, Amy Wong, Jon Burnsky, Jake Vincent, Yu’an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Lijia Sun, Yi Zhang, Saab Mansour, and Kathleen McKeown. 2024. TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization. In Proceedings of the 2024… find
  93. GLM Team, Aohan Zeng, Bin Xu, and 1 others. 2024. ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools. arXiv preprint arXiv:2406.12793.
  94. Qwen Team. 2024. QWQ: Reflect Deeply on the Boundaries of the Unknown. find
  95. Arun James Thirunavukkarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. 2023. Large language models in medicine. Nature Medicine, 29(8):1930– 1940. find
  96. Konstantinos Thomas, Giorgos Filandrianos, Maria Lymperaiou, Chrysoula Zerva, and Giorgos Stamou. 2024. " i never said that": A dataset, taxonomy and baselines on response clarity classification. In Findings of the Association for Computational Linguistics: EMNLP 2024. find
  97. James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. FEVER: a Large-scale Dataset for Fact Extraction and VERification. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vo…
  98. Yuchen Tian, Weixiang Yan, Qian Yang, Xuandong Zhao, Qian Chen, Wen Wang, Ziyang Luo, Lei Ma, and Dawn Song. 2025. CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 25300– 25308. find
  99. SMTI Tonmoy, SM Zaman, Vinija Jain, Anku Rani, Vipula Rawte, Aman Chadha, and Amitava Das. 2024. A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models. arXiv preprint arXiv:2401.01313.
  100. Miles Turpin, Julian Michael, Ethan Perez, and Samuel Bowman. 2023. Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting. In Advances in Neural Information Processing Systems, volume 36, pages 74952–74965.
  101. Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, and Jirong Wen. 2024b. A survey on large language model based autonomous agents. Frontiers of Computer Science, 18(6):186345.
  102. Jakob Warkotsch. 2018. Developing a graphical user interface for generating wikipedia lists with wikidata. find
  103. 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.
  104. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv preprint arXiv:2201.11903.
  105. Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Jie Huang, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, and Quoc V. Le. 2024b. Long-form factuality in large language models. In Advances in Neural Information Processing Systems, volume 37, pages 80756–80827.
  106. Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Edouard Grave. 2020. CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4003–4012.
  107. Jiarui Wu, Zhuo Liu, and Hangfeng He. 2025a. Mitigating Hallucinations in Multimodal Spatial Relations through Constraint-Aware Prompting. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 3450–3468. find
  108. Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, and Thang Luong. 2024. Fresh-LLMs: Refreshing Large Language Models with Search Engine Augmentation. In Findings of the Association for Computational Linguistics: ACL 2024, pages 136… find
  109. Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, and Chi Wang. 2023. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation. arXiv preprint arXiv:2308.08155.
  110. Yuxuan Wan, Wenxuan Wang, Yiliu Yang, Youliang Yuan, Jen-tse Huang, Pinjia He, Wenxiang Jiao, and Michael Lyu. 2024. LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing,… find
  111. Yuhe Wu, Yuran Chen, Zhuang Liu, and Wayne Lin. 2025b. Enhancing Financial Decision-making under Cyber Threats: a Dual-branch Framework Integrating Bayesian Deep Learning and Explainable AI. Annals of Operations Research, pages 1–33. find
  112. Bin Wang, Chao Xu, Xiaomeng Zhao, Linke Ouyang, Fan Wu, Zhiyuan Zhao, Rui Xu, Kaiwen Liu, Yuan Qu, Fukai Shang, Bo Zhang, Liqun Wei, Zhihao Sui, Wei Li, Botian Shi, Yu Qiao, Dahua Lin, and Conghui He. 2024a. MinerU: An Open-Source Solution for Precise Document Content Extraction. arXiv preprint arXi…
  113. Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, Rui Zheng, Xiaoran Fan, Xiao Wang, Limao Xiong, Yuhao Zhou, Weiran Wang, Changhao Jiang, Yicheng Zou, Xiangyang Liu, and 9 others. 2025. The rise and potential of large language model…
  114. Yi Xu, Junjie Ou, Hui Xu, and Luoyi Fu. 2023. Temporal Knowledge Graph Reasoning with Historical Contrastive Learning. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 4765–4773. find
  115. Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Ling…
  116. Jianhao Yan, Yun Luo, and Yue Zhang. 2024. RefuteBench: Evaluating Refuting Instruction-Following for Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2024, pages 13775–13791. find
  117. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2369…
  118. Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, and Hayato Yamana. 2024c. ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models. In Proceedings of the 2… find
  119. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, Hao Zhang, Joseph E Gonzalez, and Ion Stoica. 2023. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. In Advances in Neural Information Processing Systems, volume 3…
  120. Zhiying Zhu, Yiming Yang, and Zhiqing Sun. 2024. HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild. arXiv preprint arXiv:2403.04307. — [arXiv:2403.04307](https://arxiv.org/abs/2403.04307) find
  121. Lei Yu, Meng Cao, Jackie CK Cheung, and Yue Dong. 2024. Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 7943–7956. find
  122. Yuexiang Zhai, Shengbang Tong, Xiao Li, Mu Cai, Qing Qu, Yong Jae Lee, and Yi Ma. 2023. Investigating the Catastrophic Forgetting in Multimodal Large Language Models. arXiv preprint arXiv:2309.10313.
  123. Cong Zhang, Xin Deik Goh, Dexun Li, Hao Zhang, and Yong Liu. 2025a. Planning with Multi-Constraints via Collaborative Language Agents. In Proceedings of the 31st International Conference on Computational Linguistics, pages 10054–10082.
  124. Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, and Min Lin. 2024a. Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs. In Advances in Neural Information Processing Systems, volume 37, pages 333–356. find
  125. Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, and Shuming Shi. 2025b. Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models. Computational Linguistics, 51(4)…
  126. Yuji Zhang, Sha Li, Jiateng Liu, Pengfei Yu, Yi R Fung, Jing Li, Manling Li, and Heng Ji. 2024b. Knowledge Overshadowing Causes Amalgamated Hallucination in Large Language Models. arXiv preprint arXiv:2407.08039. — [arXiv:2407.08039](https://arxiv.org/abs/2407.08039) find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/wu-2026-prism-probing-reasoning-instruction-source/