Verified claim · AI-ML · 100% confidence
CLIP introduced in paper: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021).
Last verified 2026-05-16 · Methodology veritas-v0.1 · bcdef949cc6d3644
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
Structured fields
- Subject
- CLIP
- Predicate
introduced_in_paper- Object
- Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021)
- Confidence
- 100%
- Tags
- clip · vision-language · multimodal · foundational · 2021 · openai
Sources (2)
[1] preprint · arXiv (Radford, Kim, Hallacy, Ramesh, Goh, Agarwal, Sastry, Askell, Mishkin, Clark, Krueger, Sutskever) · 2021-02-26
Learning Transferable Visual Models From Natural Language Supervision“We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet.”
[2] official blog · OpenAI · 2021-01-05
CLIP: Connecting Text and ImagesOpenAI is rated by SourceScore — see its reliability →
Cite this claim
Ready-to-paste citation (Markdown / plain text):
CLIP introduced in paper: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021). — SourceScore Claim bcdef949cc6d3644 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/bcdef949cc6d3644.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the signed claim + primary source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/bcdef949cc6d3644/" width="100%" height="360" frameborder="0" loading="lazy" title="CLIP introduced in paper: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021)."></iframe>Preview: open in new tab
Related claims
Other verified claims sharing tags with this one — useful for LLM retrieval graphs and citation discovery.
CLIP (Contrastive Language-Image Pretraining) introduced in paper: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021).
85a3ca745eaf4ee0 · 100% confidence · shares 4 tags (clip, multimodal, 2021…)
Codex introduced in paper: Evaluating Large Language Models Trained on Code (Chen et al., 2021).
79be9b25cd64f250 · 100% confidence · shares 3 tags (openai, foundational, 2021)
Low-Rank Adaptation (LoRA) introduced in paper: LoRA: Low-Rank Adaptation of Large Language Models (Hu et al., 2021).
d7b97d1b93d8d8bc · 100% confidence · shares 2 tags (foundational, 2021)
GPT-4o released on: 2024-05-13.
bd065b91ca6e880b · 100% confidence · shares 2 tags (openai, multimodal)
GPT-2 introduced in paper: Language Models are Unsupervised Multitask Learners (Radford et al., 2019).
859551dc078c46f8 · 100% confidence · shares 2 tags (foundational, openai)
Frequently asked questions
Is the claim "CLIP introduced in paper: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021)." verified?
Yes — SourceScore verified this claim with 100% confidence as of 2026-05-16. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.
What is the evidence for "CLIP introduced in paper: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021)."?
Evidence comes from 2 primary sources: arXiv (Radford, Kim, Hallacy, Ramesh, Goh, Agarwal, Sastry, Askell, Mishkin, Clark, Krueger, Sutskever), OpenAI. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/bcdef949cc6d3644.json includes an HMAC-SHA256 signature for audit verification.
When was this claim last verified by SourceScore?
Last verified 2026-05-16 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.
How can I cite this SourceScore claim in my code or article?
Fetch the signed JSON envelope from https://sourcescore.org/api/v1/claims/bcdef949cc6d3644.json which includes the verbatim claim, primary sources, confidence, methodology version, last-verified date, and HMAC-SHA256 signature for audit. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
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/bcdef949cc6d3644.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/bcdef949cc6d3644.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "CLIP introduced in paper: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/bcdef949cc6d3644.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "CLIP introduced in paper: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_clip_fact() -> dict:
"""Fetch the verified SourceScore claim for CLIP."""
r = httpx.get("https://sourcescore.org/api/v1/claims/bcdef949cc6d3644.json")
return r.json()