When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

作者：Norah A. Alzahrani, H. A. Alyahya, Sultan Yazeed Alnumay, Shaykhah Z. Alsubaie, Yusef Almushaykeh, F. Mirza, Nouf M. Alotaibi, Nora Altwairesh, Areeb Alowisheq, Saiful Bari, Haidar Khan, A. Jeddah, B. Makkah, C. París, D. Riyadh, B. Riyadh, D. Makkah, Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Mostafa Dehghani, Yi Tay, A. Gritsenko, Zhe Zhao, N. Houlsby, Fernando Díaz, Donald Metzler, Leo Gao, J. Tow, Baber Abbasi, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Alain Le Noac'h, Haonan Li, Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, M. Tahmid, Rahman Laskar, Md Mizanur Rahman, Amran Bhuiyan, 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 Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, E. Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, O. Khattab, Peter Henderson, Qian Huang, Ryan A. Chi, S. Michael, S. Xie, Surya Santurkar, Tatsunori Ganguli, T. Hashimoto, Tianyi Icard, Vishrav Zhang, William Chaudhary, Xue-Cheng Wang, Yifan Li, Mai Yuhui, Zhang Yuta, Koreeda. 2023, Holistic evaluation, Colin Raffel, Noam Shazeer, A. Roberts, K. Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu, Joshua Robinson, Christopher Rytting, David Wingate, Leveraging, Amrita Saha, Vardaan Pahuja, Mitesh M. Khapra, Karthik Sankaranarayanan, Victor Sanh, Albert Webson, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGer-ald, Rahul Gupta, Wael Hamza, Charith Peris, Stephen Rawls, Andrew Rosenbaum, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Won Chung, A. Chowdhery, V. Quoc, Ed H Le, Chi, Denny, Hugo Touvron, Louis Martin, Kevin R. Stone, Peter Al-bert, Amjad Almahairi, Yasmine Babaei, Nikolay Bash-lykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, D. Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wen-Yin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, A. Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel M. Kloumann, Punit Artem Korenev, S. Koura, M. Lachaux, Thibaut Lavril, Jenya Lee, Diana 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, Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, J. Michael, Felix Hill, Omer Levy, Samuel R. Bowman, Superglue, Amanpreet Singh, Félix, Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, A. Saied, Weizhu Chen, Nan Duan. 2023 · 发表于：Annual Meeting of the Association for Computational Linguistics · 年份：2024 · DOI：10.48550/arxiv.2402.01781 · 被引用次数：175 · 研究领域：Computer Science

Large Language Model (LLM) leaderboards based on benchmark rankings are regularly used to guide practitioners in model selection. Often, the published leaderboard rankings are taken at face value - we show this is a (potentially costly) mistake. Under existing leaderboards, the relative performance of LLMs is highly sensitive to (often minute) details. We show that for popular multiple-choice question benchmarks (e.g., MMLU), minor perturbations to the benchmark, such as changing the order of choices or the method of answer selection, result in changes in rankings up to 8 positions. We explain this phenomenon by conducting systematic experiments over three broad categories of benchmark perturbations and identifying the sources of this behavior. Our analysis results in several best-practice recommendations, including the advantage of a hybrid scoring method for answer selection. Our study highlights the dangers of relying on simple benchmark evaluations and charts the path for more robust evaluation schemes on the existing benchmarks. The code for this paper is available at https://github.com/National-Center-for-AI-Saudi-Arabia/lm-evaluation-harness.