Verified claim · AI-ML · 82% confidence
BigBird introduced in paper: Big Bird: Transformers for Longer Sequences (Zaheer et al., 2020).
Last verified 2026-06-19 · Methodology veritas-v0.1 · 1783cda1b5f622e7
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Structured fields
- Subject
- BigBird
- Predicate
introduced_in_paper- Object
- Big Bird: Transformers for Longer Sequences (Zaheer et al., 2020)
- Confidence
- 82%
- Tags
- bigbird · big-bird · sparse-attention · long-sequences · transformers · nlp · 2020
Sources (2)
[1] preprint · arXiv (Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed) · 2020-07-28
Big Bird: Transformers for Longer Sequences“To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.”
[2] docs · Hugging Face
Big Bird: Transformers for Longer Sequences (Hugging Face Papers)Hugging Face is rated by SourceScore — see its reliability →
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BigBird introduced in paper: Big Bird: Transformers for Longer Sequences (Zaheer et al., 2020). — SourceScore Claim 1783cda1b5f622e7 (verified 2026-06-19). https://sourcescore.org/api/v1/claims/1783cda1b5f622e7.jsonEmbed this claim
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Frequently asked questions
Is the claim "BigBird introduced in paper: Big Bird: Transformers for Longer Sequences (Zaheer et al., 2020)." verified?
Yes — SourceScore verified this claim with 82% confidence as of 2026-06-19. 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 "BigBird introduced in paper: Big Bird: Transformers for Longer Sequences (Zaheer et al., 2020)."?
Evidence comes from 2 primary sources: arXiv (Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed), Hugging Face. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/1783cda1b5f622e7.json includes an HMAC-SHA256 signature for audit verification.
When was this claim last verified by SourceScore?
Last verified 2026-06-19 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/1783cda1b5f622e7.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.
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cURL
curl https://sourcescore.org/api/v1/claims/1783cda1b5f622e7.jsonJavaScript / TypeScript
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// "BigBird introduced in paper: Big Bird: Transformers for Longer Sequences (Zaheer et al., 2020)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/1783cda1b5f622e7.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "BigBird introduced in paper: Big Bird: Transformers for Longer Sequences (Zaheer et al., 2020)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_bigbird_fact() -> dict:
"""Fetch the verified SourceScore claim for BigBird."""
r = httpx.get("https://sourcescore.org/api/v1/claims/1783cda1b5f622e7.json")
return r.json()