Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Language Models are Few-Shot Butlers   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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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: adap-org
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The categories of this paper are: gr-qc
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: q-alg
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The categories of this paper are: ao-sci
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The categories of this paper are: econ
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con