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Language models (LMs) exhibit remarkable abilities to solve new tasks fromjust a few examples or textual instructions, especially at scale. They also,paradoxically, struggle with basic functionality, such as arithmetic or factuallookup, where much simpler and smaller models excel. In this paper, we showthat LMs can teach themselves to use external tools via simple APIs and achievethe best of both worlds. We introduce Toolformer, a model trained to decidewhich APIs to call, when to call them, what arguments to pass, and how to bestincorporate the results into future token prediction. This is done in aself-supervised way, requiring nothing more than a handful of demonstrationsfor each API. We incorporate a range of tools, including a calculator, a Q\&Asystem, two different search engines, a translation system, and a calendar.Toolformer achieves substantially improved zero-shot performance across avariety of downstream tasks, often competitive with much larger models, withoutsacrificing its core language modeling abilities.

Toolformer: Language Models Can Teach Themselves to Use Tools