Verified claim · AI-ML · 100% confidence
ColBERT introduced in: Khattab & Zaharia 2020 — late-interaction retrieval.
Last verified 2026-05-16 · Methodology veritas-v0.1 · 2335984b07f28cac
Structured fields
- Subject
- ColBERT
- Predicate
introduced_in- Object
- Khattab & Zaharia 2020 — late-interaction retrieval
- Confidence
- 100%
- Tags
- colbert · stanford · retrieval · late-interaction · foundational · sigir · 2020 · introduced_in
Sources (2)
[1] preprint · arXiv / SIGIR 2020 (Khattab, Zaharia / Stanford) · 2020-04-27
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT“We present ColBERT, a novel ranking model that adapts deep LMs (in particular, BERT) for efficient retrieval. ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity.”
[2] github release · Stanford FutureData · 2020-04-27
ColBERT — official Stanford FutureData repository
Cite this claim
Ready-to-paste citation (Markdown / plain text):
ColBERT introduced in: Khattab & Zaharia 2020 — late-interaction retrieval. — SourceScore Claim 2335984b07f28cac (verified 2026-05-16). https://sourcescore.org/api/v1/claims/2335984b07f28cac.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/2335984b07f28cac/" width="100%" height="360" frameborder="0" loading="lazy" title="ColBERT introduced in: Khattab & Zaharia 2020 — late-interaction retrieval."></iframe>Preview: open in new tab
Related claims
Other verified claims sharing tags with this one — useful for LLM retrieval graphs and citation discovery.
Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020).
d15057ced937a103 · 100% confidence · shares 3 tags (retrieval, foundational, 2020)
AlphaFold 1 introduced in: Senior et al. 2020 — DeepMind protein structure prediction.
a77a8dd48941a53d · 100% confidence · shares 3 tags (foundational, 2020, introduced_in)
Kaplan scaling laws introduced in paper: Kaplan et al. 2020 — Scaling Laws for Neural Language Models.
22e12bfbe7770657 · 100% confidence · shares 3 tags (foundational, 2020, introduced_in)
Direct Preference Optimization (DPO) introduced in paper: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (Rafailov et al., 2023).
a3e691683a4577af · 100% confidence · shares 2 tags (foundational, stanford)
Vision Transformer (ViT) introduced in paper: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Dosovitskiy et al., 2020).
d3681b0981e0b700 · 100% confidence · shares 2 tags (foundational, 2020)
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/2335984b07f28cac.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/2335984b07f28cac.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "ColBERT introduced in: Khattab & Zaharia 2020 — late-interaction retrieval."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/2335984b07f28cac.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "ColBERT introduced in: Khattab & Zaharia 2020 — late-interaction retrieval."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_colbert_fact() -> dict:
"""Fetch the verified SourceScore claim for ColBERT."""
r = httpx.get("https://sourcescore.org/api/v1/claims/2335984b07f28cac.json")
return r.json()