Fetching the paper…
Reading the bibliography…
LLM-as-a-judge models have been used for evaluating both human and AI generated content, specifically by providing scores and rationales.
Explain yourself! leveraging language models for commonsense reasoning, 2019
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher · 1906
Earlier work this paper cites.
Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko · 2007
Earlier work this paper cites.
Concrete problems in ai safety, 2016
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
Earlier work this paper cites.
Right for the right reasons: Training differentiable models by constraining their explanations, 2017
Andrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez · 2017
Earlier work this paper cites.
Language models are unsupervised multitask learners, 2019
Alec Radford, Jeff Wu, Rewon Child, D. Luan, Dario Amodei, and Ilya Sutskever · 2019
Earlier work this paper cites.
Constitutional ai: Harmlessness from ai feedback, 2022
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan · 2022
Earlier work this paper cites.
Benchmarking foundation models with language-model-as-an-examiner, 2023
Yushi Bai, Jiahao Ying, Yixin Cao, Xin Lv, Yuze He, Xiaozhi Wang, Jifan Yu, Kaisheng Zeng, Yijia Xiao, Haozhe Lyu, Jiayin Zhang, Juanzi Li, and Lei Hou · 2023
Earlier work this paper cites.
Deep reinforcement learning from human preferences, 2023
Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2023
Earlier work this paper cites.
Patrick Fernandes, Daniel Deutsch, Mara Finkelstein, Parker Riley, André F. T. Martins, Graham Neubig, Ankush Garg, Jonathan H. Clark, Markus Freitag, and Orhan Firat · 2023
Earlier work this paper cites.
Prometheus: Inducing fine-grained evaluation capability in language models, 2023
Seungone Kim, Jamin Shin, Yejin Cho, Joel Jang, Shayne Longpre, Hwaran Lee, Sangdoo Yun, Seongjin Shin, Sungdong Kim, James Thorne, and Minjoon Seo · 2023
Cited alongside, same era.
Large language models are zero-shot reasoners, 2023
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2023
Cited alongside, same era.
Generative judge for evaluating alignment, 2023
Junlong Li, Shichao Sun, Weizhe Yuan, Run-Ze Fan, Hai Zhao, and Pengfei Liu · 2023
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models, 2023
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2023
Cited alongside, same era.
Rlaif vs. rlhf: Scaling reinforcement learning from human feedback with ai feedback, 2024
Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, and Sushant Prakash · 2024
Closest in time.
Ang Li, Qiugen Xiao, Peng Cao, Jian Tang, Yi Yuan, Zijie Zhao, Xiaoyuan Chen, Liang Zhang, Xiangyang Li, Kaitong Yang, Weidong Guo, Yukang Gan, Xu Yu, Daniell Wang, and Ying Shan · 2024
Closest in time.
West-of-n: Synthetic preference generation for improved reward modeling, 2024
Alizée Pace, Jonathan Mallinson, Eric Malmi, Sebastian Krause, and Aliaksei Severyn · 2024
Closest in time.
Iterative reasoning preference optimization, 2024
Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, He He, Sainbayar Sukhbaatar, and Jason Weston · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zhipeng Chen, Kun Zhou, Wayne Xin Zhao, Junchen Wan, Fuzheng Zhang, Di Zhang, and Ji-Rong Wen · 2024
Cited alongside, same era.
The llama 3 herd of models, 2024
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, et al · 2024
Cited alongside, same era.
Bonbon alignment for large language models and the sweetness of best-of-n sampling, 2024
Lin Gui, Cristina Gârbacea, and Victor Veitch · 2024
Cited alongside, same era.
Data-centric human preference optimization with rationales, 2024
Hoang Anh Just, Ming Jin, Anit Sahu, Huy Phan, and Ruoxi Jia · 2024
Cited alongside, same era.
Rewardbench: Evaluating reward models for language modeling, 2024
Nathan Lambert, Valentina Pyatkin, Jacob Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, and Hannaneh Hajishirzi · 2024
Cited alongside, same era.
Prometheus: Inducing fine-grained evaluation capability in language models, 2024a
Seungone Kim, Jamin Shin, Yejin Cho, Joel Jang, Shayne Longpre, Hwaran Lee, Sangdoo Yun, Seongjin Shin, Sungdong Kim, James Thorne, and Minjoon Seo
Cited in the paper.
Seungone Kim, Juyoung Suk, Ji Yong Cho, Shayne Longpre, Chaeeun Kim, Dongkeun Yoon, Guijin Son, Yejin Cho, Sheikh Shafayat, Jinheon Baek, Sue Hyun Park, Hyeonbin Hwang, Jinkyung Jo, Hyowon Cho, Haebin Shin, Seongyun Lee, Hanseok Oh, Noah Lee, Namgyu Ho, Se June Joo, Miyoung Ko, Yoonjoo Lee, Hyungjoo Chae, Jamin Shin, Joel Jang, Seonghyeon Ye, Bill Yuchen Lin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, and Minjoon Seo
Cited in the paper.
Prometheus 2: An open source language model specialized in evaluating other language models, 2024c
Seungone Kim, Juyoung Suk, Shayne Longpre, Bill Yuchen Lin, Jamin Shin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, and Minjoon Seo
Cited in the paper.
Direct preference optimization: Your language model is secretly a reward model, 2024
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn · 2024
Closest in time.
Branch-solve-merge improves large language model evaluation and generation, 2024
Swarnadeep Saha, Omer Levy, Asli Celikyilmaz, Mohit Bansal, Jason Weston, and Xian Li · 2024
Closest in time.
Foundational autoraters: Taming large language models for better automatic evaluation, 2024
Tu Vu, Kalpesh Krishna, Salaheddin Alzubi, Chris Tar, Manaal Faruqui, and Yun-Hsuan Sung · 2024
Closest in time.
Meta-rewarding language models: Self-improving alignment with llm-as-a-meta-judge, 2024
Tianhao Wu, Weizhe Yuan, Olga Golovneva, Jing Xu, Yuandong Tian, Jiantao Jiao, Jason Weston, and Sainbayar Sukhbaatar · 2024
Closest in time.
Self-rewarding language models
Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Sainbayar Sukhbaatar, Jing Xu, and Jason Weston · 2024
Closest in time.