Abstract
Large language models are able to perform a task by conditioning on a few input-output demonstrations - a paradigm known as in-context learning. We show that language models can explicitly infer an underlying task from a few demonstrations by prompting them to generate a natural language instruction that fits the examples. To explore this ability, we introduce the instruction induction challenge, compile a dataset consisting of 24 tasks, and define a novel evaluation metric based on executing the generated instruction. We discover that, to a large extent, the ability to generate instructions does indeed emerge when using a model that is both large enough and aligned to follow instructions; InstructGPT achieves 65.7% of human performance in our execution-based metric, while the original GPT-3 model reaches only 9.8% of human performance. This surprising result suggests that instruction induction might be a viable learning paradigm in and of itself, where instead of fitting a set of latent continuous parameters to the data, one searches for the best description in the natural language hypothesis space.
Original language | English |
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Title of host publication | Long Papers |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 1935-1952 |
Number of pages | 18 |
ISBN (Electronic) | 9781959429722 |
State | Published - 2023 |
Event | 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada Duration: 9 Jul 2023 → 14 Jul 2023 |
Publication series
Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
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Volume | 1 |
ISSN (Print) | 0736-587X |
Conference
Conference | 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 |
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Country/Territory | Canada |
City | Toronto |
Period | 9/07/23 → 14/07/23 |
Bibliographical note
Publisher Copyright:© 2023 Association for Computational Linguistics.