Instruction Induction: From Few Examples to Natural Language Task Descriptions

• URL: http://arxiv.org/abs/2205.10782v1
• Date: Sun, 22 May 2022 09:22:37 GMT
• Title: Instruction Induction: From Few Examples to Natural Language Task Descriptions
• Authors: Or Honovich, Uri Shaham, Samuel R. Bowman, Omer Levy
• Abstract summary: 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. 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.
• Score: 55.139554327372934
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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