---
title: 'Toolformer: Language Models Can Teach Themselves to Use Tools'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/2302-04761-toolformer.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, Thomas Scialom, Toolformer: Language Models Can Teach Themselves to Use Tools (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: tools'
tags: arxiv, tools, language-models, agents
arxiv_id: 2302.04761
alias: 2302.04761-toolformer
type: pointer
format: pointer
sources:
  - id: S1
    title: 'Toolformer: Language Models Can Teach Themselves to Use Tools'
    publisher: arXiv
    authors: 'Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, Thomas Scialom'
    url: https://arxiv.org/abs/2302.04761
    version_read: 'v1 (9 Feb 2023)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# Toolformer: Language Models Can Teach Themselves to Use Tools

## Summary
[Toolformer: Language Models Can Teach Themselves to Use Tools](https://arxiv.org/abs/2302.04761) (2023) showed that a language model can learn when to call external tools — such as a calculator, a search API, or a calendar — by self-supervised training, rather than only by hand-written tool prompts.

What it introduced: a method for inserting tool calls into a model's own text, keeping the calls that actually help next-token prediction, and then fine-tuning on that self-labeled data so the model decides when to use a tool.

What it led to: a large part of the later tool-use and function-calling literature, including agents that learn or make their own tools. Live notes: [ao-tools-llms-as-tool-makers](https://iinventedlight.com/library/notes/ao-tools-llms-as-tool-makers/). For the security side of tool-using systems, see the indirect prompt-injection pointer in this batch and [ao-security-prompt-injection-design](https://iinventedlight.com/library/notes/ao-security-prompt-injection-design/).

Where to read it: https://arxiv.org/abs/2302.04761 (v1, 9 Feb 2023).

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).