Verified claim · AI-ML · 100% confidence
Codex introduced in paper: Evaluating Large Language Models Trained on Code (Chen et al., 2021).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 79be9b25cd64f250
Structured fields
- Subject
- Codex
- Predicate
introduced_in_paper- Object
- Evaluating Large Language Models Trained on Code (Chen et al., 2021)
- Confidence
- 100%
- Tags
- codex · code-generation · openai · foundational · 2021
Sources (2)
[1] preprint · arXiv (Chen, Tworek, Jun, Yuan, Pinto, Kaplan, et al.) · 2021-07-07
Evaluating Large Language Models Trained on Code“We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities.”
[2] official blog · OpenAI · 2021-08-10
OpenAI Codex
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Codex introduced in paper: Evaluating Large Language Models Trained on Code (Chen et al., 2021). — SourceScore Claim 79be9b25cd64f250 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/79be9b25cd64f250.jsonEmbed this claim
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<iframe src="https://sourcescore.org/embed/claim/79be9b25cd64f250/" width="100%" height="360" frameborder="0" loading="lazy" title="Codex introduced in paper: Evaluating Large Language Models Trained on Code (Chen et al., 2021)."></iframe>Preview: open in new tab
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Use this claim in your code
Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.
cURL
curl https://sourcescore.org/api/v1/claims/79be9b25cd64f250.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/79be9b25cd64f250.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "Codex introduced in paper: Evaluating Large Language Models Trained on Code (Chen et al., 2021)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/79be9b25cd64f250.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Codex introduced in paper: Evaluating Large Language Models Trained on Code (Chen et al., 2021)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_codex_fact() -> dict:
"""Fetch the verified SourceScore claim for Codex."""
r = httpx.get("https://sourcescore.org/api/v1/claims/79be9b25cd64f250.json")
return r.json()