Fetching the paper…
Reading the bibliography…
Using language models to scalably approximate human preferences on text quality (LLM-as-a-judge) has become a standard practice applicable to many tasks.
Task complexity and contingent processing in decision making: An information search and protocol analysis
John W. Payne. 1976 · 1976
Earlier work this paper cites.
Independence of clones as a criterion for voting rules
T. N. Tideman. 1987 · 1987
Earlier work this paper cites.
The paradox of nontransitive dice
Richard P. Savage. 1994 · 1994
Earlier work this paper cites.
Statistical properties of the sample semi-variance
Shaun A. Bond and Stephen E. Satchell. 2002 · 2002
Earlier work this paper cites.
Nontransitive dice with equal means
Mark Finkelstein and Edward O. Thorp. 2006 · 2006
Earlier work this paper cites.
A new monotonic, clone-independent, reversal symmetric, and condorcet-consistent single-winner election method
Markus Schulze. 2011 · 2011
Earlier work this paper cites.
Intransitive dice
Brian Conrey, James Gabbard, Katie Grant, Andrew Liu, and Kent E. Morrison. 2013 · 2013
Earlier work this paper cites.
Intransitivity in theory and in the real world
Alexander Y Klimenko. 2015 · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed H. Chi, F. Xia, Quoc Le, and Denny Zhou. 2022 · 2022
Earlier work this paper cites.
Rankformer: Listwise learning-to-rank using listwide labels
Maarten Buyl, Paul Missault, and Pierre-Antoine Sondag. 2023 · 2023
Earlier work this paper cites.
Chateval: Towards better llm-based evaluators through multi-agent debate
Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shan Zhang, Jie Fu, and Zhiyuan Liu. 2023 · 2023
Earlier work this paper cites.
Batch prompting: Efficient inference with large language model apis
Zhoujun Cheng, Jungo Kasai, and Tao Yu. 2023 · 2023
Earlier work this paper cites.
A closer look into automatic evaluation using large language models
Cheng-Han Chiang and Hunghuei Lee. 2023 · 2023
Earlier work this paper cites.
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
Earlier work this paper cites.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
Earlier work this paper cites.
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
Earlier work this paper cites.
Applying large language models and chain-of-thought for automatic scoring
Gyeong-Geon Lee, Ehsan Latif, Xuansheng Wu, Ninghao Liu, and Xiaoming Zhai. 2023 · 2023
Earlier work this paper cites.
Alpacaeval: An automatic evaluator of instruction-following models
Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
Earlier work this paper cites.
Hunter Lightman, Vineet Kosaraju, Yura Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. 2023 · 2023
Earlier work this paper cites.
Zero-shot nlg evaluation through pairware comparisons with llms
Adian Liusie, Potsawee Manakul, and Mark JF Gales. 2023 · 2023
Earlier work this paper cites.
Octopack: Instruction tuning code large language models
Niklas Muennighoff, Qian Liu, Qi Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, et al. 2023 · 2023
Earlier work this paper cites.
Large language models are effective text rankers with pairwise ranking prompting
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Le Yan, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, et al. 2023 · 2023
Earlier work this paper cites.
Xstest: A test suite for identifying exaggerated safety behaviours in large language models
Paul Röttger, Hannah Rose Kirk, Bertie Vidgen, Giuseppe Attanasio, Federico Bianchi, and Dirk Hovy. 2023 · 2023
Earlier work this paper cites.
Branch-solve-merge improves large language model evaluation and generation
Swarnadeep Saha, Omer Levy, Asli Celikyilmaz, Mohit Bansal, Jason Weston, and Xian Li. 2023 · 2023
Cited alongside, same era.
Distributional preference learning: Understanding and accounting for hidden context in rlhf
Anand Siththaranjan, Cassidy Laidlaw, and Dylan Hadfield-Menell. 2023 · 2023
Cited alongside, same era.
Found in the middle: Permutation self-consistency improves listwise ranking in large language models
Raphael Tang, Xinyu Crystina Zhang, Xueguang Ma, Jimmy Lin, and Ferhan Ture. 2023 · 2023
Cited alongside, same era.
Do-not-answer: A dataset for evaluating safeguards in llms
Yuxia Wang, Haonan Li, Xudong Han, Preslav Nakov, and Timothy Baldwin. 2023 · 2023
Cited alongside, same era.
Flask: Fine-grained language model evaluation based on alignment skill sets
Wildbench: Benchmarking llms with challenging tasks from real users in the wild
Bill Yuchen Lin, Yuntian Deng, Khyathi Chandu, Faeze Brahman, Abhilasha Ravichander, Valentina Pyatkin, Nouha Dziri, Ronan Le Bras, and Yejin Choi. 2024 · 2024
Later among the works it cites.
Efficient llm comparative assessment: A product of experts framework for pairwise comparisons
Adian Liusie, Vatsal Raina, Yassir Fathullah, and Mark J. F. Gales. 2024 · 2024
Later among the works it cites.
Are language model logits calibrated?
Charles Lovering, Michael Krumdick, Viet Dac Lai, Nilesh Kumar, Varshini Reddy, Rik Koncel-Kedziorski, and Chris Tanner. 2024 · 2024
Later among the works it cites.
Regression aware inference with llms
Michal Lukasik, Harikrishna Narasimhan, Aditya Krishna Menon, Felix X. Yu, and Sanjiv Kumar. 2024 · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Seonghyeon Ye, Doyoung Kim, Sungdong Kim, Hyeonbin Hwang, Seungone Kim, Yongrae Jo, James Thorne, Juho Kim, and Minjoon Seo. 2023 · 2023
Cited alongside, same era.
Evaluating large language models at evaluating instruction following
Zhiyuan Zeng, Jiatong Yu, Tianyu Gao, Yu Meng, Tanya Goyal, and Danqi Chen. 2023 · 2023
Cited alongside, same era.
Judgelm: Fine-tuned large language models are scalable judges
Lianghui Zhu, Xinggang Wang, and Xinlong Wang. 2023 · 2023
Cited alongside, same era.
A setwise approach for effective and highly efficient zero-shot ranking with large language models
Shengyao Zhuang, Honglei Zhuang, Bevan Koopman, and G. Zuccon. 2023 · 2023
Cited alongside, same era.
Critique-out-loud reward models
Zachary Ankner, Mansheej Paul, Brandon Cui, Jonathan D Chang, and Prithviraj Ammanabrolu. 2024 · 2024
Cited alongside, same era.
Ju-Seung Byun, Jiyun Chun, Jihyung Kil, and Andrew Perrault. 2024 · 2024
Cited alongside, same era.
Compassjudger-1: All-in-one judge model helps model evaluation and evolution
Maosong Cao, Alexander Lam, Haodong Duan, Hongwei Liu, Songyang Zhang, and Kai Chen. 2024 · 2024
Cited alongside, same era.
Quantile regression for distributional reward models in rlhf
Nicolai Dorka. 2024 · 2024
Cited alongside, same era.
Dakota Mahan, Duy Van Phung, Rafael Rafailov, Chase Blagden, Nathan Lile, Louis Castricato, Jan-Philipp Fränken, Chelsea Finn, and Alon Albalak. 2024 · 2024
Later among the works it cites.
Benchmarking distributional alignment of large language models
Nicole Meister, Carlos Guestrin, and Tatsunori Hashimoto. 2024 · 2024
Later among the works it cites.
OpenAI, :, Aaron Hurst, Adam Lerer, Adam P. Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, et al. 2024 · 2024
Later among the works it cites.
Beyond the binary: Capturing diverse preferences with reward regularization
Vishakh Padmakumar, Chuanyang Jin, Hannah Rose Kirk, and He He. 2024 · 2024
Later among the works it cites.
Personalizing reinforcement learning from human feedback with variational preference learning
Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, and Natasha Jaques. 2024 · 2024
Later among the works it cites.
Lampo: Large language models as preference machines for few-shot ordinal classification
Zhen Qin, Junru Wu, Jiaming Shen, Tianqi Liu, and Xuanhui Wang. 2024 · 2024
Later among the works it cites.
Is llm-as-a-judge robust? investigating universal adversarial attacks on zero-shot llm assessment
Vyas Raina, Adian Liusie, and Mark Gales. 2024 · 2024
Later among the works it cites.
First: Faster improved listwise reranking with single token decoding
Revanth Gangi Reddy, JaeHyeok Doo, Yifei Xu, Md Arafat Sultan, Deevya Swain, Avirup Sil, and Heng Ji. 2024 · 2024
Later among the works it cites.
Lmunit: Fine-grained evaluation with natural language unit tests
Jon Saad-Falcon, Rajan Vivek, William Berrios, Nandita Shankar Naik, Matija Franklin, Bertie Vidgen, Amanpreet Singh, Douwe Kiela, and Shikib Mehri. 2024 · 2024
Later among the works it cites.
Cbeval: A framework for evaluating and interpreting cognitive biases in llms
Ammar Shaikh, Raj Abhijit Dandekar, Sreedath Panat, and Raj Abhijit Dandekar. 2024 · 2024
Later among the works it cites.
Lin Shi, Weicheng Ma, and Soroush Vosoughi. 2024 · 2024
Later among the works it cites.
Position: A roadmap to pluralistic alignment
Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell L Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, et al. 2024 · 2024
Later among the works it cites.
To cot or not to cot? chain-of-thought helps mainly on math and symbolic reasoning
Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, Dongwei Jiang, Manya Wadhwa, Prasann Singhal, Xinyu Zhao, Xi Ye, Kyle Mahowald, and Greg Durrett. 2024 · 2024
Later among the works it cites.
Large language models are inconsistent and biased evaluators
Rickard Stureborg, Dimitris Alikaniotis, and Yoshi Suhara. 2024 · 2024
Later among the works it cites.
Judgebench: A benchmark for evaluating llm-based judges
Sijun Tan, Siyuan Zhuang, Kyle Montgomery, William Y Tang, Alejandro Cuadron, Chenguang Wang, Raluca Ada Popa, and Ion Stoica. 2024 · 2024
Later among the works it cites.
Chain-of-thought reasoning without prompting
Xuezhi Wang and Denny Zhou. 2024 · 2024
Later among the works it cites.
Alma: Alignment with minimal annotation
Michihiro Yasunaga, Leonid Shamis, Chunting Zhou, Andrew Cohen, Jason Weston, Luke Zettlemoyer, and Marjan Ghazvininejad. 2024 · 2024
Later among the works it cites.
Krystian Zawistowski. 2024 · 2024
Later among the works it cites.
Online self-preferring language models
Yuanzhao Zhai, Zhuo Zhang, Kele Xu, Hanyang Peng, Yue Yu, Dawei Feng, Cheng Yang, Bo Ding, and Huaimin Wang. 2024 · 2024
Later among the works it cites.
Starling-7b: Improving helpfulness and harmlessness with rlaif
Banghua Zhu, Evan Frick, Tianhao Wu, Hanlin Zhu, Karthik Ganesan, Wei-Lin Chiang, Jian Zhang, and Jiantao Jiao. 2024 · 2024
Later among the works it cites.