Verified claim · AI-ML · 100% confidence
Reformer introduced in paper: Reformer: The Efficient Transformer (Kitaev, Kaiser, Levskaya, 2020).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 76f7f00e79bc18c8
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
- Reformer
- Predicate
introduced_in_paper- Object
- Reformer: The Efficient Transformer (Kitaev, Kaiser, Levskaya, 2020)
- Confidence
- 100%
- Tags
- reformer · efficient-transformer · lsh-attention · foundational · 2020 · iclr · google
Sources (2)
[1] preprint · arXiv (Kitaev, Kaiser, Levskaya) · 2020-01-13
Reformer: The Efficient Transformer“We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its complexity from O(L^2) to O(L log L), where L is the length of the sequence.”
[2] peer reviewed · OpenReview / ICLR · 2020-04-26
Reformer: The Efficient Transformer (ICLR 2020)
Cite this claim
Ready-to-paste citation (Markdown / plain text):
Reformer introduced in paper: Reformer: The Efficient Transformer (Kitaev, Kaiser, Levskaya, 2020). — SourceScore Claim 76f7f00e79bc18c8 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/76f7f00e79bc18c8.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/76f7f00e79bc18c8/" width="100%" height="360" frameborder="0" loading="lazy" title="Reformer introduced in paper: Reformer: The Efficient Transformer (Kitaev, Kaiser, Levskaya, 2020)."></iframe>Preview: open in new tab
Related claims
Other verified claims sharing tags with this one — useful for LLM retrieval graphs and citation discovery.
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 4 tags (foundational, 2020, google…)
ELECTRA introduced in paper: ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators (Clark et al., 2020).
2f9c79357e9d4da9 · 100% confidence · shares 3 tags (foundational, 2020, google)
Mixture of Experts (MoE) revival popularized in: Shazeer et al. 2017 — outrageously large neural networks via sparse gating.
f068236101568ad7 · 100% confidence · shares 3 tags (google, foundational, iclr)
Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020).
d15057ced937a103 · 100% confidence · shares 2 tags (foundational, 2020)
BERT (Bidirectional Encoder Representations from Transformers) introduced in paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., 2018).
4c1ee70007dc89c1 · 100% confidence · shares 2 tags (foundational, google)
Frequently asked questions
Is the claim "Reformer introduced in paper: Reformer: The Efficient Transformer (Kitaev, Kaiser, Levskaya, 2020)." 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 "Reformer introduced in paper: Reformer: The Efficient Transformer (Kitaev, Kaiser, Levskaya, 2020)."?
Evidence comes from 2 primary sources: arXiv (Kitaev, Kaiser, Levskaya), OpenReview / ICLR. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/76f7f00e79bc18c8.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/76f7f00e79bc18c8.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/76f7f00e79bc18c8.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/76f7f00e79bc18c8.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "Reformer introduced in paper: Reformer: The Efficient Transformer (Kitaev, Kaiser, Levskaya, 2020)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/76f7f00e79bc18c8.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Reformer introduced in paper: Reformer: The Efficient Transformer (Kitaev, Kaiser, Levskaya, 2020)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_reformer_fact() -> dict:
"""Fetch the verified SourceScore claim for Reformer."""
r = httpx.get("https://sourcescore.org/api/v1/claims/76f7f00e79bc18c8.json")
return r.json()