Language Models are Few-Shot Butlers

• URL: http://arxiv.org/abs/2104.07972v1
• Date: Fri, 16 Apr 2021 08:47:07 GMT
• Title: Language Models are Few-Shot Butlers
• Authors: Vincent Micheli, Fran\c{c}ois Fleuret
• Abstract summary: We introduce a two-stage procedure to learn from a small set of demonstrations and further improve by interacting with an environment. We show that language models fine-tuned with only 1.2% of the expert demonstrations and a simple reinforcement learning algorithm achieve a 51% absolute improvement in success rate over existing methods in the ALFWorld environment.
• Score: 0.2538209532048867
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Pretrained language models demonstrate strong performance in most NLP tasks when fine-tuned on small task-specific datasets. Hence, these autoregressive models constitute ideal agents to operate in text-based environments where language understanding and generative capabilities are essential. Nonetheless, collecting expert demonstrations in such environments is a time-consuming endeavour. We introduce a two-stage procedure to learn from a small set of demonstrations and further improve by interacting with an environment. We show that language models fine-tuned with only 1.2% of the expert demonstrations and a simple reinforcement learning algorithm achieve a 51% absolute improvement in success rate over existing methods in the ALFWorld environment.

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