Toolformer: Language Models Can Teach Themselves to Use Tools
Where tool calling comes from. Instead of computing five minus three in the weights, the model emits a call and the harness runs it. Declare the tools in the system prompt and the action space is suddenly whatever you are willing to execute.
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The model learns when to call an API, not just how to answer.
Tools are self-supervised: the calls that reduce loss on the continuation are the ones kept.
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.