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Computer Science > Computation and Language

arXiv:2302.04761 (cs)
[Submitted on 9 Feb 2023]

Title:Toolformer: Language Models Can Teach Themselves to Use Tools

Authors:Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu
, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, Thomas Scialom
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    Abstract: Language models (LMs) exhibit remarkable abilities to
    solve new tasks from just a few examples or textual instructions,
    especially at scale. They also, paradoxically, struggle with
    basic functionality, such as arithmetic or factual lookup, where
    much simpler and smaller models excel. In this paper, we show
    that LMs can teach themselves to use external tools via simple
    APIs and achieve the best of both worlds. We introduce
    Toolformer, a model trained to decide which APIs to call, when to
    call them, what arguments to pass, and how to best incorporate
    the results into future token prediction. This is done in a
    self-supervised way, requiring nothing more than a handful of
    demonstrations for each API. We incorporate a range of tools,
    including a calculator, a Q\&A system, two different search
    engines, a translation system, and a calendar. Toolformer
    achieves substantially improved zero-shot performance across a
    variety of downstream tasks, often competitive with much larger
    models, without sacrificing its core language modeling abilities.

Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2302.04761 [cs.CL]
          (or arXiv:2302.04761v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2302.04761
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Timo Schick [view email]
[v1] Thu, 9 Feb 2023 16:49:57 UTC (202 KB)
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