TuringAdvice: A Generative and Dynamic Evaluation of Language Use

Rowan Zellers, Ari Holtzman, Elizabeth Clark, Lianhui Qin, Ali Farhadi, Yejin Choi


Abstract
We propose TuringAdvice, a new challenge task and dataset for language understanding models. Given a written situation that a real person is currently facing, a model must generate helpful advice in natural language. Our evaluation framework tests a fundamental aspect of human language understanding: our ability to use language to resolve open-ended situations by communicating with each other. Empirical results show that today’s models struggle at TuringAdvice, even multibillion parameter models finetuned on 600k in-domain training examples. The best model, T5, writes advice that is at least as helpful as human-written advice in only 14% of cases; a much larger non-finetunable GPT3 model does even worse at 4%. This low performance reveals language understanding errors that are hard to spot outside of a generative setting, showing much room for progress.
Anthology ID:
2021.naacl-main.386
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4856–4880
Language:
URL:
https://aclanthology.org/2021.naacl-main.386
DOI:
10.18653/v1/2021.naacl-main.386
Bibkey:
Cite (ACL):
Rowan Zellers, Ari Holtzman, Elizabeth Clark, Lianhui Qin, Ali Farhadi, and Yejin Choi. 2021. TuringAdvice: A Generative and Dynamic Evaluation of Language Use. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4856–4880, Online. Association for Computational Linguistics.
Cite (Informal):
TuringAdvice: A Generative and Dynamic Evaluation of Language Use (Zellers et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.386.pdf
Data
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