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Evaluation of natural language generation (NLG) is complex and multi-dimensional.
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.
Summeval: Re-evaluating summarization evaluation
Alexander R Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2020 · 2007
Earlier work this paper cites.
Evaluating coherence in dialogue systems using entailment
Nouha Dziri, Ehsan Kamalloo, Kory Mathewson, and Osmar Zaiane. 2019 · 2019
Earlier work this paper cites.
MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance
Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M. Meyer, and Steffen Eger. 2019 · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Earlier work this paper cites.
GRADE: Automatic graph-enhanced coherence metric for evaluating open-domain dialogue systems
Lishan Huang, Zheng Ye, Jinghui Qin, Liang Lin, and Xiaodan Liang. 2020 · 2020
Earlier work this paper cites.
Evaluating the factual consistency of abstractive text summarization
Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2020 · 2020
Cited alongside, same era.
BLEURT: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
Cited alongside, same era.
Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2020
Cited alongside, same era.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
Cited alongside, same era.
Compression, transduction, and creation: A unified framework for evaluating natural language generation
Mingkai Deng, Bowen Tan, Zhengzhong Liu, Eric Xing, and Zhiting Hu. 2021 · 2021
Cited alongside, same era.
Annotating and modeling fine-grained factuality in summarization
ExplainaBoard: An explainable leaderboard for NLP
Pengfei Liu, Jinlan Fu, Yang Xiao, Weizhe Yuan, Shuaichen Chang, Junqi Dai, Yixin Liu, Zihuiwen Ye, and Graham Neubig. 2021 · 2021
Later among the works it cites.
Prompt programming for large language models: Beyond the few-shot paradigm
Laria Reynolds and Kyle McDonell. 2021 · 2021
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Bartscore: Evaluating generated text as text generation
Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021 · 2021
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News summarization and evaluation in the era of gpt-3
Tanya Goyal, Junyi Jessy Li, and Greg Durrett. 2022 · 2022
Later among the works it cites.
Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Later among the works it cites.
Towards a unified multi-dimensional evaluator for text generation
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Tanya Goyal and Greg Durrett. 2021 · 2021
Cited alongside, same era.
Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, Pengfei Liu, Chenguang Zhu, Heng Ji, and Jiawei Han. 2022 · 2022
Later among the works it cites.