Verified claim · AI-ML · 100% confidence
Toolformer introduced in: Schick et al. 2023 — self-supervised LLM tool-use.
Last verified 2026-05-16 · Methodology veritas-v0.1 · cd4387e16e2c3e3d
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
Related verified claims
More verified claims related to this one — keep exploring.
OpenAI Function Calling publicly released on: 2023-06-13 by OpenAI.
100% confidence · shares 3 tags (function-calling, tool-use, 2023)
ReAct (Reasoning + Acting) introduced in paper: ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al., 2022).
100% confidence · shares 2 tags (agents, tool-use)
vLLM introduced in: Kwon et al. 2023 — high-throughput LLM serving via PagedAttention.
100% confidence · shares 2 tags (2023, introduced_in)
Chatbot Arena introduced in: Zheng et al. 2023 — LMSYS open platform for evaluating LLMs by human preference.
100% confidence · shares 2 tags (2023, introduced_in)
FAISS introduced in: Johnson, Douze, Jégou 2017 — Facebook AI Similarity Search.
100% confidence · shares 2 tags (meta-ai, introduced_in)
Self-RAG introduced in: Asai et al. 2023 — self-reflective retrieval-augmented generation.
100% confidence · shares 2 tags (2023, introduced_in)
AlpacaEval introduced in: Li et al. 2023 — LLM-as-judge evaluation benchmark.
100% confidence · shares 2 tags (2023, introduced_in)
OpenAI Assistants API publicly released on: 2023-11-06 by OpenAI.
100% confidence · shares 2 tags (agents, 2023)
Structured fields
- Subject
- Toolformer
- Predicate
introduced_in- Object
- Schick et al. 2023 — self-supervised LLM tool-use
- Confidence
- 100%
- Tags
- toolformer · meta-ai · tool-use · function-calling · agents · 2023 · introduced_in
Sources (2)
[1] preprint · arXiv (Schick, Dwivedi-Yu, Dessì, Raileanu, Lomeli, Zettlemoyer, Cancedda, Scialom / Meta AI) · 2023-02-09
Toolformer: Language Models Can Teach Themselves to Use Tools“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.”
[2] official blog · Meta AI · 2023-02-09
Toolformer — Meta AI Research publication
Cite this claim
Ready-to-paste citation (Markdown / plain text):
Toolformer introduced in: Schick et al. 2023 — self-supervised LLM tool-use. — SourceScore Claim cd4387e16e2c3e3d (verified 2026-05-16). https://sourcescore.org/api/v1/claims/cd4387e16e2c3e3d.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the claim record + top cited source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/cd4387e16e2c3e3d/" width="100%" height="360" frameborder="0" loading="lazy" title="Toolformer introduced in: Schick et al. 2023 — self-supervised LLM tool-use."></iframe>Preview: open in new tab
Frequently asked questions
How has SourceScore reviewed the claim "Toolformer introduced in: Schick et al. 2023 — self-supervised LLM tool-use."?
SourceScore records this assertion with 100% legacy editorial confidence as of 2026-05-16, under methodology veritas-v0.1. That metadata is not a truth guarantee. Inspect the 2 cited source record(s), excerpts, and live evidence below.
What is the evidence for "Toolformer introduced in: Schick et al. 2023 — self-supervised LLM tool-use."?
The record lists 2 cited source(s): arXiv (Schick, Dwivedi-Yu, Dessì, Raileanu, Lomeli, Zettlemoyer, Cancedda, Scialom / Meta AI), Meta AI. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/cd4387e16e2c3e3d.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.
When was this claim record last reviewed by SourceScore?
Last reviewed 2026-05-16 under methodology version veritas-v0.1. The dated JSON record includes SourceScore-issued HMAC metadata, which is not publicly recomputable. Refetch the record and inspect current evidence before relying on it.
How can I cite this SourceScore claim in my code or article?
Fetch the JSON record from https://sourcescore.org/api/v1/claims/cd4387e16e2c3e3d.json, including the verbatim claim, cited evidence, confidence, methodology version, and last-verified date. Refetch the canonical HTTPS record and inspect cited evidence; the HMAC tag is not publicly independently verifiable. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this record from your application. The response includes verbatim excerpts, primary-source URLs, and SourceScore-issued HMAC integrity metadata. Refetch the canonical HTTPS record and inspect cited evidence; public users cannot independently verify the HMAC tag.
cURL
curl https://sourcescore.org/api/v1/claims/cd4387e16e2c3e3d.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/cd4387e16e2c3e3d.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "Toolformer introduced in: Schick et al. 2023 — self-supervised LLM tool-use."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/cd4387e16e2c3e3d.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Toolformer introduced in: Schick et al. 2023 — self-supervised LLM tool-use."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_toolformer_fact() -> dict:
"""Fetch the verified SourceScore claim for Toolformer."""
r = httpx.get("https://sourcescore.org/api/v1/claims/cd4387e16e2c3e3d.json")
return r.json()