TuringAdvice: A Generative and Dynamic Evaluation of Language Use
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, a finetuned 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.
- Publication:
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arXiv e-prints
- Pub Date:
- April 2020
- DOI:
- 10.48550/arXiv.2004.03607
- arXiv:
- arXiv:2004.03607
- Bibcode:
- 2020arXiv200403607Z
- Keywords:
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- Computer Science - Computation and Language
- E-Print:
- NAACL 2021 camera ready. Project page at https://rowanzellers.com/advice