@inproceedings{clark-etal-2021-thats,
title = "All That{'}s {`}Human{'} Is Not Gold: Evaluating Human Evaluation of Generated Text",
author = "Clark, Elizabeth and
August, Tal and
Serrano, Sofia and
Haduong, Nikita and
Gururangan, Suchin and
Smith, Noah A.",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.565",
doi = "10.18653/v1/2021.acl-long.565",
pages = "7282--7296",
abstract = "Human evaluations are typically considered the gold standard in natural language generation, but as models{'} fluency improves, how well can evaluators detect and judge machine-generated text? We run a study assessing non-experts{'} ability to distinguish between human- and machine-authored text (GPT2 and GPT3) in three domains (stories, news articles, and recipes). We find that, without training, evaluators distinguished between GPT3- and human-authored text at random chance level. We explore three approaches for quickly training evaluators to better identify GPT3-authored text (detailed instructions, annotated examples, and paired examples) and find that while evaluators{'} accuracy improved up to 55{\%}, it did not significantly improve across the three domains. Given the inconsistent results across text domains and the often contradictory reasons evaluators gave for their judgments, we examine the role untrained human evaluations play in NLG evaluation and provide recommendations to NLG researchers for improving human evaluations of text generated from state-of-the-art models.",
}
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%0 Conference Proceedings
%T All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text
%A Clark, Elizabeth
%A August, Tal
%A Serrano, Sofia
%A Haduong, Nikita
%A Gururangan, Suchin
%A Smith, Noah A.
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F clark-etal-2021-thats
%X Human evaluations are typically considered the gold standard in natural language generation, but as models’ fluency improves, how well can evaluators detect and judge machine-generated text? We run a study assessing non-experts’ ability to distinguish between human- and machine-authored text (GPT2 and GPT3) in three domains (stories, news articles, and recipes). We find that, without training, evaluators distinguished between GPT3- and human-authored text at random chance level. We explore three approaches for quickly training evaluators to better identify GPT3-authored text (detailed instructions, annotated examples, and paired examples) and find that while evaluators’ accuracy improved up to 55%, it did not significantly improve across the three domains. Given the inconsistent results across text domains and the often contradictory reasons evaluators gave for their judgments, we examine the role untrained human evaluations play in NLG evaluation and provide recommendations to NLG researchers for improving human evaluations of text generated from state-of-the-art models.
%R 10.18653/v1/2021.acl-long.565
%U https://aclanthology.org/2021.acl-long.565
%U https://doi.org/10.18653/v1/2021.acl-long.565
%P 7282-7296
Markdown (Informal)
[All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text](https://aclanthology.org/2021.acl-long.565) (Clark et al., ACL-IJCNLP 2021)
ACL
- Elizabeth Clark, Tal August, Sofia Serrano, Nikita Haduong, Suchin Gururangan, and Noah A. Smith. 2021. All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 7282–7296, Online. Association for Computational Linguistics.