CoChat
Sign Up
Cited by 121Artificial Intelligence ApplicationsScientific Computing and Data ManagementEthics and Social Impacts of AI

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices

Open access
Reuel, Anka, Hardy, Amelia, Smith, Chandler, Lamparth, Max, Hardy, Malcolm, Kochenderfer, Mykel·2024·Advances in Neural Information Processing Systems 37
DOI 10.52202/079017-0685
Open this paper in CoChat — free accountOpen PDF

Abstract

AI models are increasingly prevalent in high-stakes environments, necessitating thorough assessment of their capabilities and risks. Benchmarks are popular for measuring these attributes and for comparing model performance, tracking progress, and identifying weaknesses in foundation and non-foundation models. They can inform model selection for downstream tasks and influence policy initiatives. However, not all benchmarks are the same: their quality depends on their design and usability. In this paper, we develop an assessment framework considering 46 best practices across an AI benchmark's lifecycle and evaluate 24 AI benchmarks against it. We find that there exist large quality differences and that commonly used benchmarks suffer from significant issues. We further find that most benchmarks do not report statistical significance of their results nor allow for their results to be easily replicated. To support benchmark developers in aligning with best practices, we provide a checklist for minimum quality assurance based on our assessment. We also develop a living repository of benchmark assessments to support benchmark comparability, accessible at betterbench.stanford.edu.

Overview

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.52202/079017-0685

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

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.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Poor data quality and limited reproducibility can lead to unreliable evaluations and undermine scientific credibility.

    “poor data quality and limited reproducibility can lead to unreliable evaluations and undermine scientific credibility.”

    From The Leaderboard Illusion (2025) · cites this paper as Reuel et al., 2024

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 76

  1. Abubakar Abid, Maheen Farooqi, and James Zou. Persistent anti-muslim bias in large language models. In AAAI/ACM Conference on AI, Ethics, and Society, pages 298-306, 2021.
  2. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. GPT-4 Technical Report. arXiv preprint arXiv:2303.08774, 2023. · arXiv
  3. Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Belle- mare. Deep reinforcement learning at the edge of the statistical precipice. Advances in neural information processing systems, 34:29304-29320, 2021.
  4. Parveen Azam Ali and Roger Watson. Peer review and the publication process. Nursing open, 3(4):193-202, 2016.
  5. Norah Alzahrani, Hisham Abdullah Alyahya, et al. When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards. ArXiv, abs/2402.01781, 2024. · arXiv
  6. Mohamed Radhouene Aniba, Olivier Poch, and Julie D. Thompson. Issues in bioinformat- ics benchmarking: the case study of multiple sequence alignment. Nucleic Acids Research, 38(21):7353-7363, 07 2010.
  7. David Baidoo-Anu and Leticia Owusu Ansah. Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1):52-62, 2023.
  8. Thomas Bartz-Beielstein, Carola Doerr, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, Manuel López-Ibáñez, Katherine Mary Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, and T. Weise. Benchmarking in Optimization: Best Practice and Open Issues. ArXiv, abs/2007.03488, 2020. · arXiv
  9. M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling. The Arcade Learning Environment: An Evaluation Platform for General Agents. Journal of Artificial Intelligence Research, 47:253-279, jun 2013.
  10. Eshta Bhardwaj, Harshit Gujral, Siyi Wu, Ciara Zogheib, Tegan Maharaj, and Christoph Becker. Machine learning data practices through a data curation lens: An evaluation framework. In The 2024 ACM Conference on Fairness, Accountability, and Transparency, pages 1055-1067, 2024.
  11. Stella Biderman, Hailey Schoelkopf, Lintang Sutawika, Leo Gao, Jonathan Tow, Baber Abbasi, Alham Fikri Aji, Pawan Sasanka Ammanamanchi, Sidney Black, Jordan Clive, Anthony DiPofi, Julen Etxaniz, Benjamin Fattori, Jessica Zosa Forde, Charles Foster, Jeffrey Hsu, Mimansa Jaiswal, Wilson Y. Lee, Haonan Li, Charles Lovering, Niklas Muennighoff, Ellie Pavlick, Jason Phang, Aviya Skowron, Samson Tan, Xiangru Tang, Kevin A. Wang, Genta Indra Winata, François Yvon, and Andy Zou. Lessons from the Trenches on Reproducible Evaluation of Language Models. arXiv preprint arXiv:2405.14782, 2024. · arXiv
  12. Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021. · arXiv
  13. Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, and Xing Xie. A Survey on Evaluation of Large Language Models. ACM Trans. Intell. Syst. Technol., 15(3), mar 2024.
  14. Peter M Chapman. Environmental Quality Benchmarks -The Good, The Bad, and The Ugly. Environmental Science and Pollution Research, 25(4):3043-3046, 2018.
  15. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. Evaluating Large Language Models Trained on Code. arXiv preprint arXiv:2107.03374, 2021. · arXiv
  16. Zhihui Cheng, Chin-Sheng Pang, Peiqi Wang, Son T Le, Yanqing Wu, Davood Shahrjerdi, Iuliana Radu, Max C Lemme, Lian-Mao Peng, Xiangfeng Duan, et al. How to report and benchmark emerging field-effect transistors. Nature Electronics, 5(7):416-423, 2022.
  17. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge. arXiv preprint arXiv:1803.05457, 2018. · arXiv
  18. Karl Cobbe, Christopher Hesse, Jacob Hilton, and John Schulman. Leveraging procedural generation to benchmark reinforcement learning. In Proceedings of the 37th International Conference on Machine Learning, ICML'20. JMLR.org, 2020.
  19. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training Verifiers to Solve Math Word Problems. arXiv preprint arXiv:2110.14168, 2021. · arXiv
  20. Lee J Cronbach and Paul E Meehl. Construct validity in psychological tests. Psychological bulletin, 52(4):281, 1955.
  21. David Dalrymple, Joar Skalse, Yoshua Bengio, Stuart Russell, Max Tegmark, Sanjit Seshia, Steve Omohundro, Christian Szegedy, Ben Goldhaber, Nora Ammann, et al. Towards Guar- anteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems. arXiv preprint arXiv:2405.06624, 2024. · arXiv
  22. Niek F de Jonge, Kevin Mildau, David Meijer, Joris JR Louwen, Christoph Bueschl, Florian Huber, and Justin JJ van der Hooft. Good practices and recommendations for using and bench- marking computational metabolomics metabolite annotation tools. Metabolomics, 18(12):103, 2022.
  23. Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta. BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT '21, page 862-872, New York, NY, USA, 2021. Association for Computing Machinery.
  24. Shahriar Golchin and Mihai Surdeanu. Time Travel in LLMs: Tracing Data Contamination in Large Language Models. CoRR, abs/2308.08493, 2023. · arXiv
  25. Declan Grabb, Max Lamparth, and Nina Vasan. Risks from Language Models for Automated Mental Healthcare: Ethics and Structure for Implementation. In First Conference on Language Modeling, 2024.
  26. Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zołna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Nicolas Heess, and Nando de Freitas. RL unplugged: a suite of benchmarks for offline reinforcement learning. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS '20, Red Hook, NY, USA, 2020.
  27. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring Massive Multitask Language Understanding. In International Conference on Learning Representations (ICLR), 2021.
  28. Alon Jacovi, Avi Caciularu, Omer Goldman, and Yoav Goldberg. Stop uploading test data in plain text: Practical strategies for mitigating data contamination by evaluation benchmarks. arXiv preprint arXiv:2305.10160, 2023. · arXiv
  29. Minhao Jiang, Ken Ziyu Liu, Ming Zhong, Rylan Schaeffer, Siru Ouyang, Jiawei Han, and Sanmi Koyejo. Investigating Data Contamination for Pre-training Language Models. ArXiv, abs/2401.06059, 2024. · arXiv
  30. Minqi Jiang, Jelena Luketina, Nantas Nardelli, Pasquale Minervini, Philip H.S. Torr, Shimon Whiteson, and Tim Rocktäschel. WordCraft: An Environment for Benchmarking Commonsense Agents. In Workshop on Language in Reinforcement Learning (LaRel), 2020.
  31. Kevin B Johnson, Wei-Qi Wei, Dilhan Weeraratne, Mark E Frisse, Karl Misulis, Kyu Rhee, Juan Zhao, and Jane L Snowdon. Precision medicine, AI, and the future of personalized health care. Clinical and translational science, 14(1):86-93, 2021.
  32. Keller Jordan. Calibrated chaos: Variance between runs of neural network training is harmless and inevitable. arXiv preprint arXiv:2304.01910, 2023. · arXiv
  33. Sayash Kapoor, Peter Henderson, and Arvind Narayanan. Promises and pitfalls of artificial intelligence for legal applications. arXiv preprint arXiv:2402.01656, 2024. · arXiv
  34. Sayash Kapoor and Arvind Narayanan. Leakage and the reproducibility crisis in machine- learning-based science. Patterns, 4(9):100804, 2023.
  35. Vasileia Karasavva and Aalia Noorbhai. The real threat of deepfake pornography: A review of canadian policy. Cyberpsychology, Behavior, and Social Networking, 24(3):203-209, 2021.
  36. Max Lamparth, Anthony Corso, Jacob Ganz, Oriana Skylar Mastro, Jacquelyn Schneider, and Harold Trinkunas. Human vs. Machine: Behavioral Differences Between Expert Humans and Language Models in Wargame Simulations. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2024.
  37. David J Lewkowicz. The Concept of Ecological Validity: What Are Its Limitations and Is It Bad to Be Invalid? Infancy, 2(4):437-450, 2001.
  38. Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Alexander Cosgrove, Christopher D Manning, Christopher Re, Diana Acosta-Navas, Drew Arad Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue WANG, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Andrew Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, and Yuta Koreeda. Holistic Evaluation of Language Models. Transactions on Machine Learning Research, 2023. Featured Certification, Expert Certification.
  39. Q Vera Liao and Ziang Xiao. Rethinking Model Evaluation as Narrowing The Socio-Technical Gap. In Workshop on Artificial Intelligence & Human Computer Interaction, volume 1, 2024.
  40. Stephanie Lin, Jacob Hilton, and Owain Evans. TruthfulQA: Measuring How Models Mimic Human Falsehoods. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio, editors, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3214-3252, Dublin, Ireland, May 2022. Association for Computational Linguistics.
  41. Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, and Jie Tang. AgentBench: Evaluating LLMs as Agents. In The Twelfth International Conference on Learning Representations, 2024.
  42. Xiao-Yang Liu, Ziyi Xia, Jingyang Rui, Jiechao Gao, Hongyang Yang, Ming Zhu, Christina Dan Wang, Zhaoran Wang, and Jian Guo. FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2022.
  43. Yu Lu Liu, Su Lin Blodgett, Jackie Chi Kit Cheung, Q Vera Liao, Alexandra Olteanu, and Ziang Xiao. ECBD: Evidence-Centered Benchmark Design for NLP. arXiv preprint arXiv:2406.08723, 2024. · arXiv
  44. Manikanta Loya, Divya Anand Sinha, and Richard Futrell. Exploring the Sensitivity of LLMs' Decision-Making Capabilities: Insights from Prompt Variation and Hyperparameters. arXiv preprint arXiv:2312.17476, 2023. · arXiv
  45. MingYu Lu, Zachary Shahn, Daby Sow, Finale Doshi-Velez, and H Lehman Li-wei. Is deep reinforcement learning ready for practical applications in healthcare? a sensitivity analysis of duel-ddqn for hemodynamic management in sepsis patients. In AMIA Annual Symposium Proceedings, volume 2020, page 773. American Medical Informatics Association, 2020.
  46. Kit-Kay Mak, Yi-Hang Wong, and Mallikarjuna Rao Pichika. Artificial intelligence in drug discovery and development. Drug Discovery and Evaluation: Safety and Pharmacokinetic Assays, pages 1-38, 2023.
  47. Timothy R McIntosh, Teo Susnjak, Tong Liu, Paul Watters, and Malka N Halgamuge. Inad- equacies of large language model benchmarks in the era of generative artificial intelligence. arXiv preprint arXiv:2402.09880, 2024. · arXiv
  48. Robert J Mislevy, Linda S Steinberg, and Russell G Almond. Focus article: On the structure of educational assessments. Measurement: Interdisciplinary research and perspectives, 1(1):3-62, 2003.
  49. Simon Ott, Adriano Barbosa-Silva, Kathrin Blagec, Jan Brauner, and Matthias Samwald. Mapping global dynamics of benchmark creation and saturation in artificial intelligence. Nature Communications, 13(1):6793.
  50. Alexander Pan, Jun Shern Chan, Andy Zou, Nathaniel Li, Steven Basart, Thomas Woodside, Hanlin Zhang, Scott Emmons, and Dan Hendrycks. Do the rewards justify the means? mea- suring trade-offs between rewards and ethical behavior in the MACHIAVELLI benchmark. In Proceedings of the 40th International Conference on Machine Learning, ICML'23. JMLR.org, 2023.
  51. Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thomp- son, Phu Mon Htut, and Samuel Bowman. BBQ: A hand-built bias benchmark for question answering. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio, editors, Findings of the Association for Computational Linguistics: ACL 2022, pages 2086-2105, Dublin, Ireland, May 2022. Association for Computational Linguistics.
  52. Mahima Pushkarna, Andrew Zaldivar, and Oddur Kjartansson. Data cards: Purposeful and trans- parent dataset documentation for responsible ai. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 1776-1826, 2022.
  53. E Fantin Irudaya Raj, M Appadurai, and K Athiappan. Precision farming in modern agriculture. In Smart Agriculture Automation Using Advanced Technologies: Data Analytics and Machine Learning, Cloud Architecture, Automation and IoT, pages 61-87. Springer, 2022.
  54. Deborah Raji, Emily Denton, Emily M. Bender, Alex Hanna, and Amandalynne Paullada. AI and the Everything in the Whole Wide World Benchmark. In J. Vanschoren and S. Yeung, editors, Advances in Neural Information Processing Systems (NeurIPS), 2021.
  55. David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R Bowman. GPQA: A graduate-level google-proof q&a benchmark. arXiv preprint arXiv:2311.12022, 2023. · arXiv
  56. Anka Reuel, Lisa Soder, Benjamin Bucknall, and Trond Arne Undheim. Position: Technical Research and Talent is Needed for Effective AI Governance. Forty-first International Conference on Machine Learning, 2024.
  57. Juan-Pablo Rivera, Gabriel Mukobi, Anka Reuel, Max Lamparth, Chandler Smith, and Jacque- lyn Schneider. Escalation risks from language models in military and diplomatic decision- making. In The 2024 ACM Conference on Fairness, Accountability, and Transparency, pages 836-898, 2024.
  58. Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. WinoGrande: an adversarial winograd schema challenge at scale. Commun. ACM, 64(9):99-106, aug 2021.
  59. Atsushi Shirafuji, Takumi Ito, Makoto Morishita, Yuki Nakamura, Yusuke Oda, Jun Suzuki, and Yutaka Watanobe. Prompt sensitivity of language model for solving programming problems. In New Trends in Intelligent Software Methodologies, Tools and Techniques, pages 346-359. IOS Press, 2022.
  60. Aryan Shrivastava, Jessica Hullman, and Max Lamparth. Measuring Free-Form Decision- Making Inconsistency of Language Models in Military Crisis Simulations. arXiv preprint arXiv:2410.13204, 2024. · arXiv
  61. Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al. Beyond the Imitation Game: Quanti- fying and extrapolating the capabilities of language models. Transactions on Machine Learning Research, 2023.
  62. Bernd Carsten Stahl and David Wright. Ethics and privacy in AI and big data: Implementing responsible research and innovation. IEEE Security & Privacy, 16(3):26-33, 2018.
  63. Arjun Subramonian, Xingdi Yuan, Hal Daumé III, and Su Lin Blodgett. It takes two to tango: Navigating conceptualizations of NLP tasks and measurements of performance. arXiv preprint arXiv:2305.09022, 2023. · arXiv
  64. Johannes Söding and Michael Remmert. Protein sequence comparison and fold recognition: progress and good-practice benchmarking. Current Opinion in Structural Biology, 21(3):404- 411, 2011.
  65. Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Dan MacKinlay, Francesco Alesiani, Dirk Pflüger, and Mathias Niepert. PDEBench: An Extensive Benchmark for Scientific Machine Learning. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2022.
  66. Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. · arXiv
  67. Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Max Bartolo, Borhane Blili-Hamelin, Kurt Bollacker, Rishi Bomassani, Marisa Ferrara Boston, Siméon Campos, Kal Chakra, Canyu Chen, Cody Coleman, Zacharie Delpierre Coudert, Leon Derczynski, Debojyoti Dutta, Ian Eisenberg, James Ezick, Heather Frase, Brian Fuller, Ram Gandikota, Agasthya Gangavarapu, Ananya Gangavarapu, James Gealy, Rajat Ghosh, James Goel, Usman Gohar, Sujata Goswami, Scott A. Hale, Wiebke Hutiri, Joseph Marvin Imperial, Surgan Jandial, Nick Judd, Felix Juefei-Xu, Foutse Khomh, Bhavya Kailkhura, Hannah Rose Kirk, Kevin Klyman, Chris Knotz, Michael Kuchnik, Shachi H. Kumar, Srijan Kumar, Chris Lengerich, Bo Li, Zeyi Liao, Eileen Peters Long, Victor Lu, Sarah Luger, Yifan Mai, Priyanka Mary Mammen, Kelvin Manyeki, Sean McGregor, Virendra Mehta, Shafee Mohammed, Emanuel Moss, Lama Nachman, Dinesh Ji- nenhally Naganna, Amin Nikanjam, Besmira Nushi, Luis Oala, Iftach Orr, Alicia Parrish, Cigdem Patlak, William Pietri, Forough Poursabzi-Sangdeh, Eleonora Presani, Fabrizio Puletti, Paul Röttger, Saurav Sahay, Tim Santos, Nino Scherrer, Alice Schoenauer Sebag, Patrick Schramowski, Abolfazl Shahbazi, Vin Sharma, Xudong Shen, Vamsi Sistla, Leonard Tang, Davide Testuggine, Vithursan Thangarasa, Elizabeth Anne Watkins, Rebecca Weiss, Chris Welty, Tyler Wilbers, Adina Williams, Carole-Jean Wu, Poonam Yadav, Xianjun Yang, Yi Zeng, Wenhui Zhang, Fedor Zhdanov, Jiacheng Zhu, Percy Liang, Peter Mattson, and Joaquin Van- schoren. Introducing v0.5 of the AI Safety Benchmark from MLCommons. arXiv preprint arXiv:2404.12241, 2024. · arXiv
  68. Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, and Bo Li. DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models. In Advances in Neural Information Processing Systems (NeurIPS), 2023.
  69. Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, et al. Taxonomy of risks posed by language models. In ACM Conference on Fairness, Accountability, and Transparency, pages 214-229, 2022.
  70. Mark D Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E Bourne, et al. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3(1):1-9, 2016.
  71. Chejian Xu, Wenhao Ding, Weijie Lyu, Zuxin Liu, Shuai Wang, Yihan He, Hanjiang Hu, Ding Zhao, and Bo Li. SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2022.
  72. Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, and Bingbing Ni. MedMNIST v2 -A large-scale lightweight benchmark for 2D and 3D biomedical image classification. Scientific Data, 10(1):41, 2023.
  73. Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Mm-vet: Evaluating large multimodal models for integrated capabilities. arXiv preprint arXiv:2308.02490, 2023. · arXiv
  74. Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, Cong Wei, Botao Yu, Ruibin Yuan, Renliang Sun, Ming Yin, Boyuan Zheng, Zhenzhu Yang, Yibo Liu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen. MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI. In Proceedings of CVPR, 2024.
  75. Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. HellaSwag: Can a Machine Really Finish Your Sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019.
  76. Kun Zhou, Yutao Zhu, Zhipeng Chen, Wentong Chen, Wayne Xin Zhao, Xu Chen, Yankai Lin, Ji-Rong Wen, and Jiawei Han. Don't Make Your LLM an Evaluation Benchmark Cheater. ArXiv, abs/2311.01964, 2023. · arXiv

Cited by

Sorted by citations

Metadata

Authors
Reuel, A., Hardy, A., Smith, C., Lamparth, M., Hardy, M., & Kochenderfer, M.
Venue
Advances in Neural Information Processing Systems 37
Publisher
Neural Information Processing Systems Foundation, Inc. (NeurIPS)
Published
2024
Pages
21763-21813
ISSN
1049-5258 (ISSN-L)
DOI
10.52202/079017-0685
OpenAlex
W4415798415
Cited by
121
References
2
Citations by year
2026: 86 · 2025: 35
FWCI
19.10
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • neural information processing systems (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Keywords
AI benchmarks, benchmark evaluation, benchmarking methodology, AI evaluation

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