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Large language model (LLM) evaluations typically rely on aggregated metrics like accuracy or human preference, averaging across users and prompts.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E Terry · 1952
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Individual choice behavior , volume 4
R Duncan Luce · 1959
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Ties in paired-comparison experiments: A generalization of the bradley-terry model
PV Rao and Lawrence L Kupper · 1967
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An algorithm with guaranteed convergence for finding a zero of a function
Richard P. Brent · 1973
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The analysis of permutations
Robin L Plackett · 1975
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Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Jürgen Schmidhuber · 1987
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Varying-coefficient models
Trevor Hastie and Robert Tibshirani · 1993
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Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Generalized linear models
Peter McCullagh · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Michael Mazeika, Dawn Song, and Jacob Steinhardt · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde Pinto, Jared Kaplan, Harrison 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 Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew Carr, Jan Leike, Josh Achiam, Vedant Mishra, Evan Morikawa, Catherine Olsson, Jakub Pachocki, Jack Hewitt, Bowen DasSarma, Sam McCandlish, Dario Amodei, and Tom Brown · 2021
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Training verifiers to solve math word problems
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 · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al · 2022
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Holistic evaluation of language models
Percy Liang et al · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Deep reinforcement learning from human preferences
Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2023
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Llm-blender: Ensembling large language models with pairwise ranking and generative fusion
Dongfu Jiang, Xiang Ren, and Bill Yuchen Lin · 2023
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Toxicchat: Analyzing the patterns of toxic behaviors in open-source LLM chat logs
Zhixing Lin et al · 2023
Routerdc: Query-based router by dual contrastive learning for assembling large language models, 2024
Shuhao Chen, Weisen Jiang, Baijiong Lin, James T. Kwok, and Yu Zhang · 2024
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Chatbot Arena: An open platform for evaluating LLMs by human preference
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael Jordan, Joseph E Gonzalez, et al · 2024
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Hybrid LLM: Cost-efficient and quality-aware query routing
Dujian Ding, Ankur Mallick, Chi Wang, Robert Sim, Subhabrata Mukherjee, Victor Ruhle, Laks VS Lakshmanan, and Ahmed Hassan Awadallah · 2024
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Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al · 2024
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Automix: Automatically mixing language models
Aman Madaan, Pranjal Aggarwal, Ankit Anand, Srividya Pranavi Potharaju, Swaroop Mishra, Pei Zhou, Aditya Gupta, Dheeraj Rajagopal, Karthik Kappaganthu, Yiming Yang, et al · 2023
Cited alongside, same era.
GPQA: A graduate-level Google-proof Q&A benchmark
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R Bowman · 2023
Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava et al · 2023
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Agieval: A human-centric benchmark for evaluating foundation models
Wanjun Zhong et al · 2023
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Principled reinforcement learning with human feedback from pairwise or k k -wise comparisons
Banghua Zhu, Jiantao Jiao, and Michael I Jordan · 2023
Cited alongside, same era.
Llama 3 model card
AI@Meta · 2024
Cited alongside, same era.
SmolLM2 - with great data, comes great performance, 2024
Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, Gabriel Martín Blázquez, Lewis Tunstall, Agustín Piqueres, Andres Marafioti, Cyril Zakka, Leandro von Werra, and Thomas Wolf · 2024
Cited alongside, same era.
Mixtral of experts, 2024
Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2024
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Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al · 2024
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RouteLLM: Learning to route LLMs with preference data
Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E Gonzalez, M Waleed Kadous, and Ion Stoica · 2024
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New models and developer products announced at DevDay, 2023
OpenAI · 2024
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Hello GPT-4o
OpenAI · 2024
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Qwen2.5: A party of foundation models, September 2024
Qwen Team · 2024
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MMLU-Pro: A more robust and challenging multi-task language understanding benchmark
Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, et al · 2024
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Livebench: A challenging, contamination-free llm benchmark
Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Ben Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Siddartha Naidu, et al · 2024
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Yi: Open Foundation Models by 01.AI, 2024
Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, Kaidong Yu, Peng Liu, Qiang Liu, Shawn Yue, Senbin Yang, Shiming Yang, Tao Yu, Wen Xie, Wenhao Huang, Xiaohui Hu, Xiaoyi Ren, Xinyao Niu, Pengcheng Nie, Yuchi Xu, Yudong Liu, Yue Wang, Yuxuan Cai, Zhenyu Gu, Zhiyuan Liu, and Zonghong Dai · 2024
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Athene-70b: Redefining the boundaries of post-training for open models
Evan Frick, Peter Jin, Tianle Li, Karthik Ganesan, Jian Zhang, Jiantao Jiao, and Banghua Zhu · 2025
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