Verified claim · AI-ML · 100% confidence
Rotary Position Embedding (RoPE) introduced in paper: RoFormer: Enhanced Transformer with Rotary Position Embedding (Su et al., 2021).
Last verified 2026-05-16 · Methodology veritas-v0.1 · f8d64457ba9fd35b
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.
Transformer architecture introduced in paper: Attention Is All You Need (Vaswani et al., 2017).
100% confidence · shares 2 tags (transformer, foundational)
Low-Rank Adaptation (LoRA) introduced in paper: LoRA: Low-Rank Adaptation of Large Language Models (Hu et al., 2021).
100% confidence · shares 2 tags (foundational, 2021)
Switch Transformer introduced in paper: Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity (Fedus et al., 2021).
100% confidence · shares 2 tags (foundational, 2021)
LoRA (Low-Rank Adaptation) introduced in paper: LoRA: Low-Rank Adaptation of Large Language Models (Hu et al., 2021).
100% confidence · shares 2 tags (foundational, 2021)
CLIP introduced in paper: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021).
100% confidence · shares 2 tags (foundational, 2021)
Latent Diffusion Models (LDM) introduced in paper: High-Resolution Image Synthesis with Latent Diffusion Models (Rombach et al., 2021).
100% confidence · shares 2 tags (foundational, 2021)
Codex introduced in paper: Evaluating Large Language Models Trained on Code (Chen et al., 2021).
100% confidence · shares 2 tags (foundational, 2021)
Reinforcement Learning from Human Feedback (RLHF) introduced in paper: Deep Reinforcement Learning from Human Preferences (Christiano et al., 2017).
100% confidence · shares 1 tag (foundational)
Structured fields
- Subject
- Rotary Position Embedding (RoPE)
- Predicate
introduced_in_paper- Object
- RoFormer: Enhanced Transformer with Rotary Position Embedding (Su et al., 2021)
- Confidence
- 100%
- Tags
- rope · position-embedding · transformer · foundational · 2021
Sources (2)
[1] preprint · arXiv (Su, Lu, Pan, Murtadha, Wen, Liu) · 2021-04-20
RoFormer: Enhanced Transformer with Rotary Position Embedding“In this paper, we first investigate various methods to integrate positional information into the learning process of transformer-based language models. Then, we propose a novel method named Rotary Position Embedding (RoPE) to effectively leverage the positional information.”
[2] github release · Zhuiyi Technology · 2021-04-20
ZhuiyiTechnology/roformer — official implementation
Cite this claim
Ready-to-paste citation (Markdown / plain text):
Rotary Position Embedding (RoPE) introduced in paper: RoFormer: Enhanced Transformer with Rotary Position Embedding (Su et al., 2021). — SourceScore Claim f8d64457ba9fd35b (verified 2026-05-16). https://sourcescore.org/api/v1/claims/f8d64457ba9fd35b.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/f8d64457ba9fd35b/" width="100%" height="360" frameborder="0" loading="lazy" title="Rotary Position Embedding (RoPE) introduced in paper: RoFormer: Enhanced Transformer with Rotary Position Embedding (Su et al., 2021)."></iframe>Preview: open in new tab
Frequently asked questions
How has SourceScore reviewed the claim "Rotary Position Embedding (RoPE) introduced in paper: RoFormer: Enhanced Transformer with Rotary Position Embedding (Su et al., 2021)."?
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 2 cited source record(s), excerpts, and live evidence below.
What is the evidence for "Rotary Position Embedding (RoPE) introduced in paper: RoFormer: Enhanced Transformer with Rotary Position Embedding (Su et al., 2021)."?
The record lists 2 cited source(s): arXiv (Su, Lu, Pan, Murtadha, Wen, Liu), Zhuiyi Technology. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/f8d64457ba9fd35b.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/f8d64457ba9fd35b.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/f8d64457ba9fd35b.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/f8d64457ba9fd35b.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "Rotary Position Embedding (RoPE) introduced in paper: RoFormer: Enhanced Transformer with Rotary Position Embedding (Su et al., 2021)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/f8d64457ba9fd35b.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Rotary Position Embedding (RoPE) introduced in paper: RoFormer: Enhanced Transformer with Rotary Position Embedding (Su et al., 2021)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_rotary_position_embedding_rope_fact() -> dict:
"""Fetch the verified SourceScore claim for Rotary Position Embedding (RoPE)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/f8d64457ba9fd35b.json")
return r.json()