Verified claim · AI-ML · 100% confidence
Transformer architecture introduced in paper: Attention Is All You Need (Vaswani et al., 2017).
Last verified 2026-05-16 · Methodology veritas-v0.1 · ad17e76a8baad7a1
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
Related verified claims
More verified claims related to this one — keep exploring.
Reinforcement Learning from Human Feedback (RLHF) introduced in paper: Deep Reinforcement Learning from Human Preferences (Christiano et al., 2017).
100% confidence · shares 3 tags (foundational, 2017, nips)
Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020).
100% confidence · shares 2 tags (foundational, nips)
Direct Preference Optimization (DPO) introduced in paper: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (Rafailov et al., 2023).
100% confidence · shares 2 tags (foundational, nips)
Sparsely-Gated Mixture-of-Experts (MoE) introduced in paper: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (Shazeer et al., 2017).
100% confidence · shares 2 tags (foundational, 2017)
Chinchilla scaling laws introduced in paper: Training Compute-Optimal Large Language Models (Hoffmann et al., 2022).
100% confidence · shares 2 tags (foundational, nips)
Proximal Policy Optimization (PPO) introduced in paper: Proximal Policy Optimization Algorithms (Schulman et al., 2017).
100% confidence · shares 2 tags (foundational, 2017)
Chain-of-Thought prompting introduced in paper: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022).
100% confidence · shares 2 tags (foundational, nips)
AlexNet introduced in paper: ImageNet Classification with Deep Convolutional Neural Networks (Krizhevsky, Sutskever, Hinton, 2012).
100% confidence · shares 2 tags (foundational, nips)
Structured fields
- Subject
- Transformer architecture
- Predicate
introduced_in_paper- Object
- Attention Is All You Need (Vaswani et al., 2017)
- Confidence
- 100%
- Tags
- transformer · attention · foundational · vaswani · 2017 · nips
Sources (3)
[1] preprint · arXiv (Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin) · 2017-06-12
Attention Is All You Need“We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.”
[2] peer reviewed · NeurIPS Foundation · 2017-12-04
Attention Is All You Need (NeurIPS 2017 proceedings)[3] official blog · Google Research · 2017-06-12
Attention Is All You Need (Google Research publication index)
Cite this claim
Ready-to-paste citation (Markdown / plain text):
Transformer architecture introduced in paper: Attention Is All You Need (Vaswani et al., 2017). — SourceScore Claim ad17e76a8baad7a1 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/ad17e76a8baad7a1.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the claim record + top cited source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/ad17e76a8baad7a1/" width="100%" height="360" frameborder="0" loading="lazy" title="Transformer architecture introduced in paper: Attention Is All You Need (Vaswani et al., 2017)."></iframe>Preview: open in new tab
Frequently asked questions
How has SourceScore reviewed the claim "Transformer architecture introduced in paper: Attention Is All You Need (Vaswani et al., 2017)."?
SourceScore records this assertion with 100% legacy editorial confidence as of 2026-05-16, under methodology veritas-v0.1. That metadata is not a truth guarantee. Inspect the 3 cited source record(s), excerpts, and live evidence below.
What is the evidence for "Transformer architecture introduced in paper: Attention Is All You Need (Vaswani et al., 2017)."?
The record lists 3 cited source(s): arXiv (Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin), NeurIPS Foundation, Google Research. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/ad17e76a8baad7a1.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.
When was this claim record last reviewed by SourceScore?
Last reviewed 2026-05-16 under methodology version veritas-v0.1. The dated JSON record includes SourceScore-issued HMAC metadata, which is not publicly recomputable. Refetch the record and inspect current evidence before relying on it.
How can I cite this SourceScore claim in my code or article?
Fetch the JSON record from https://sourcescore.org/api/v1/claims/ad17e76a8baad7a1.json, including the verbatim claim, cited evidence, confidence, methodology version, and last-verified date. Refetch the canonical HTTPS record and inspect cited evidence; the HMAC tag is not publicly independently verifiable. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this record from your application. The response includes verbatim excerpts, primary-source URLs, and SourceScore-issued HMAC integrity metadata. Refetch the canonical HTTPS record and inspect cited evidence; public users cannot independently verify the HMAC tag.
cURL
curl https://sourcescore.org/api/v1/claims/ad17e76a8baad7a1.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/ad17e76a8baad7a1.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "Transformer architecture introduced in paper: Attention Is All You Need (Vaswani et al., 2017)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/ad17e76a8baad7a1.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Transformer architecture introduced in paper: Attention Is All You Need (Vaswani et al., 2017)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_transformer_architecture_fact() -> dict:
"""Fetch the verified SourceScore claim for Transformer architecture."""
r = httpx.get("https://sourcescore.org/api/v1/claims/ad17e76a8baad7a1.json")
return r.json()