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Traditional reference-based metrics, such as BLEU and ROUGE, are less effective for assessing outputs from Large Language Models (LLMs) that produce highly creative or superior-quality text, or in situations where reference outputs are unavailable.
Advancing the rationality debate
Keith E. Stanovich and Richard F. West. 2000 · 2000
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
The what-if tool: Interactive probing of machine learning models
James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Viégas, and Jimbo Wilson. 2019 · 2019
Earlier work this paper cites.
Errudite: Scalable, reproducible, and testable error analysis
Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel S Weld. 2019 · 2019
Earlier work this paper cites.
Semi-automated data labeling
Michael Desmond, Evelyn Duesterwald, Kristina Brimijoin, Michelle Brachman, and Qian Pan. 2021 · 2020
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On training instance selection for few-shot neural text generation
Ernie Chang, Xiaoyu Shen, Hui-Syuan Yeh, and Vera Demberg. 2021 · 2021
Earlier work this paper cites.
Results of wmt22 metrics shared task: Stop using bleu–neural metrics are better and more robust
Markus Freitag, Ricardo Rei, Nitika Mathur, Chi-kiu Lo, Craig Stewart, Eleftherios Avramidis, Tom Kocmi, George Foster, Alon Lavie, and André FT Martins. 2022 · 2022
Cited alongside, same era.
Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al. 2022 · 2022
Cited alongside, same era.
Selective annotation makes language models better few-shot learners
Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu. 2022 · 2022
Cited alongside, same era.
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
Cited alongside, same era.
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
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Savvas Petridis, Ben Wedin, James Wexler, Aaron Donsbach, Mahima Pushkarna, Nitesh Goyal, Carrie J Cai, and Michael Terry. 2023 · 2023
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Interface design for crowdsourcing hierarchical multi-label text annotations
Rickard Stureborg, Bhuwan Dhingra, and Jun Yang. 2023 · 2023
Later among the works it cites.
Evaluating nlg evaluation metrics: A measurement theory perspective
Ziang Xiao, Susu Zhang, Vivian Lai, and Q Vera Liao. 2023 · 2023
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Ángel Alexander Cabrera, Erica Fu, Donald Bertucci, Kenneth Holstein, Ameet Talwalkar, Jason I Hong, and Adam Perer. 2023 · 2023
Cited alongside, same era.
Gptscore: Evaluate as you desire
Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu. 2023 · 2023
Cited alongside, same era.
Repairing the cracked foundation: A survey of obstacles in evaluation practices for generated text
Sebastian Gehrmann, Elizabeth Clark, and Thibault Sellam. 2023 · 2023
Cited alongside, same era.
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. 2023a
Cited in the paper.
Evallm: Interactive evaluation of large language model prompts on user-defined criteria
Tae Soo Kim, Yoonjoo Lee, Jamin Shin, Young-Ho Kim, and Juho Kim. 2023b
Cited in the paper.
Evallm: Interactive evaluation of large language model prompts on user-defined criteria
Tae Soo Kim, Yoonjoo Lee, Jamin Shin, Young-Ho Kim, and Juho Kim. 2023c
Cited in the paper.
Gpteval: Nlg evaluation using gpt-4 with better human alignment
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023a
Cited in the paper.
Calibrating llm-based evaluator
Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, and Qi Zhang. 2023b
Cited in the paper.
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 2023 · 2023
Later among the works it cites.
Evalullm: Llm assisted evaluation of generative outputs
Michael Desmond, Zahra Ashktorab, Qian Pan, Casey Dugan, and James M. Johnson. 2024 · 2024
Closest in time.
Self-rewarding language models
Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Sainbayar Sukhbaatar, Jing Xu, and Jason Weston. 2024 · 2024
Closest in time.