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Auto-evaluation is crucial for assessing response quality and offering feedback for model development.
Perspectives on Probability Judgment Calibration , chapter 9
Dale Griffin and Lyle Brenner. 2008 · 2008
Earlier work this paper cites.
Estimation from pairwise comparisons: Sharp minimax bounds with topology dependence
Nihar B Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramch, Martin J Wainwright, et al. 2016 · 2016
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Your 2 is my 1, your 3 is my 9: Handling arbitrary miscalibrations in ratings
Jingyan Wang and Nihar B Shah. 2019 · 2019
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Active ordinal querying for tuplewise similarity learning
Gregory Canal, Stefano Fenu, and Christopher Rozell. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Improving maximum likelihood training for text generation with density ratio estimation
Yuxuan Song, Ning Miao, Hao Zhou, Lantao Yu, Mingxuan Wang, and Lei Li. 2020 · 2020
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Muppet: Massive multi-task representations with pre-finetuning
Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, Xilun Chen, Luke Zettlemoyer, and Sonal Gupta. 2021 · 2021
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A general language assistant as a laboratory for alignment
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2021 · 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 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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Rl4f: Generating natural language feedback with reinforcement learning for repairing model outputs
Afra Feyza Akyürek, Ekin Akyürek, Aman Madaan, Ashwin Kalyan, Peter Clark, Derry Wijaya, and Niket Tandon. 2023 · 2023
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Can large language models be an alternative to human evaluations?
Cheng-Han Chiang and Hung-Yi Lee. 2023 · 2023
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SEAHORSE: A multilingual, multifaceted dataset for summarization evaluation
Elizabeth Clark, Shruti Rijhwani, Sebastian Gehrmann, Joshua Maynez, Roee Aharoni, Vitaly Nikolaev, Thibault Sellam, Aditya Siddhant, Dipanjan Das, and Ankur Parikh. 2023 · 2023
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Ultrafeedback: Boosting language models with high-quality feedback
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun. 2023 · 2023
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Beavertails: Towards improved safety alignment of llm via a human-preference dataset
Jiaming Ji, Mickel Liu, Josef Dai, Xuehai Pan, Chi Zhang, Ce Bian, Boyuan Chen, Ruiyang Sun, Yizhou Wang, and Yaodong Yang. 2023 · 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 · 2023
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Prometheus: Inducing fine-grained evaluation capability in language models
Seungone Kim, Jamin Shin, Yejin Cho, Joel Jang, Shayne Longpre, Hwaran Lee, Sangdoo Yun, Seongjin Shin, Sungdong Kim, James Thorne, et al. 2023 · 2023
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Benchmarking cognitive biases in large language models as evaluators
Ryan Koo, Minhwa Lee, Vipul Raheja, Jong Inn Park, Zae Myung Kim, and Dongyeop Kang. 2023 · 2023
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HaluEval: A large-scale hallucination evaluation benchmark for large language models
Junyi Li, Xiaoxue Cheng, Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2023b · 2023
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G-eval: Nlg evaluation using gpt-4 with better human alignment
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023b · 2023
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Self: Language-driven self-evolution for large language model
Jianqiao Lu, Wanjun Zhong, Wenyong Huang, Yufei Wang, Fei Mi, Baojun Wang, Weichao Wang, Lifeng Shang, and Qun Liu. 2023 · 2023
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SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models
Potsawee Manakul, Adian Liusie, and Mark Gales. 2023 · 2023
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Coffee: Boost your code llms by fixing bugs with feedback
Seungjun Moon, Yongho Song, Hyungjoo Chae, Dongjin Kang, Taeyoon Kwon, Kai Tzu-iunn Ong, Seung-won Hwang, and Jinyoung Yeo. 2023 · 2023
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Octopack: Instruction tuning code large language models
Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro Von Werra, and Shayne Longpre. 2023 · 2023
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Gpt-4 technical report
Yujun Mao, Yoon Kim, and Yilun Zhou. 2024 · 2024
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Simpo: Simple preference optimization with a reference-free reward
Yu Meng, Mengzhou Xia, and Danqi Chen. 2024 · 2024
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Smaug: Fixing failure modes of preference optimisation with dpo-positive
Arka Pal, Deep Karkhanis, Samuel Dooley, Manley Roberts, Siddartha Naidu, and Colin White. 2024 · 2024
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Iterative reasoning preference optimization
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OpenAI. 2023 · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023 · 2023
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A critical evaluation of evaluations for long-form question answering
Fangyuan Xu, Yixiao Song, Mohit Iyyer, and Eunsol Choi. 2023 · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
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 · 2023
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Nemotron-4 340b technical report
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Benchmarking foundation models with language-model-as-an-examiner
Yushi Bai, Jiahao Ying, Yixin Cao, Xin Lv, Yuze He, Xiaozhi Wang, Jifan Yu, Kaisheng Zeng, Yijia Xiao, Haozhe Lyu, et al. 2024 · 2024
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Offsetbias: Leveraging debiased data for tuning evaluators
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Direct preference optimization: Your language model is secretly a reward model
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Skywork critic model series
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Foundational autoraters: Taming large language models for better automatic evaluation
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Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
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Inference-time scaling for generalist reward modeling
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