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
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)
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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()