Verified claim · AI-ML · 100% confidence
RoBERTa introduced in: Liu et al. 2019 — A Robustly Optimized BERT Pretraining Approach.
Last verified 2026-05-16 · Methodology veritas-v0.1 · d4fecb26a4c9cdca
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.
DistilBERT introduced in: Sanh et al. 2019 — a smaller, faster, cheaper BERT via knowledge distillation.
100% confidence · shares 4 tags (bert, foundational, 2019…)
BART introduced in: Lewis et al. 2019 — denoising sequence-to-sequence pretraining.
100% confidence · shares 4 tags (facebook-ai, foundational, 2019…)
ARC-AGI benchmark introduced in: Chollet 2019 — abstraction and reasoning corpus.
100% confidence · shares 3 tags (foundational, 2019, introduced_in)
PEFT (parameter-efficient fine-tuning) popularized in: Houlsby et al. 2019 — Adapter Modules + downstream PEFT library.
100% confidence · shares 3 tags (foundational, 2019, introduced_in)
ALBERT introduced in paper: ALBERT: A Lite BERT for Self-supervised Learning of Language Representations (Lan et al., 2019).
82% confidence · shares 3 tags (bert, foundational, 2019)
BERT (Bidirectional Encoder Representations from Transformers) introduced in paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., 2018).
100% confidence · shares 2 tags (bert, foundational)
GPT-2 introduced in paper: Language Models are Unsupervised Multitask Learners (Radford et al., 2019).
100% confidence · shares 2 tags (foundational, 2019)
T5 (Text-to-Text Transfer Transformer) introduced in paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Raffel et al., 2019).
100% confidence · shares 2 tags (foundational, 2019)
Structured fields
- Subject
- RoBERTa
- Predicate
introduced_in- Object
- Liu et al. 2019 — A Robustly Optimized BERT Pretraining Approach
- Confidence
- 100%
- Tags
- roberta · bert · facebook-ai · pretraining · foundational · 2019 · introduced_in
Sources (2)
[1] preprint · arXiv (Liu, Ott, Goyal, Du, Joshi, Chen, Levy, Lewis, Zettlemoyer, Stoyanov / Facebook AI) · 2019-07-26
RoBERTa: A Robustly Optimized BERT Pretraining Approach“We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it.”
[2] official blog · Hugging Face · 2019-07-26
RoBERTa — Hugging Face Transformers documentationHugging Face is rated by SourceScore — see its reliability →
Cite this claim
Ready-to-paste citation (Markdown / plain text):
RoBERTa introduced in: Liu et al. 2019 — A Robustly Optimized BERT Pretraining Approach. — SourceScore Claim d4fecb26a4c9cdca (verified 2026-05-16). https://sourcescore.org/api/v1/claims/d4fecb26a4c9cdca.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/d4fecb26a4c9cdca/" width="100%" height="360" frameborder="0" loading="lazy" title="RoBERTa introduced in: Liu et al. 2019 — A Robustly Optimized BERT Pretraining Approach."></iframe>Preview: open in new tab
Frequently asked questions
How has SourceScore reviewed the claim "RoBERTa introduced in: Liu et al. 2019 — A Robustly Optimized BERT Pretraining Approach."?
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 "RoBERTa introduced in: Liu et al. 2019 — A Robustly Optimized BERT Pretraining Approach."?
The record lists 2 cited source(s): arXiv (Liu, Ott, Goyal, Du, Joshi, Chen, Levy, Lewis, Zettlemoyer, Stoyanov / Facebook AI), Hugging Face. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/d4fecb26a4c9cdca.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/d4fecb26a4c9cdca.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/d4fecb26a4c9cdca.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/d4fecb26a4c9cdca.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "RoBERTa introduced in: Liu et al. 2019 — A Robustly Optimized BERT Pretraining Approach."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/d4fecb26a4c9cdca.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "RoBERTa introduced in: Liu et al. 2019 — A Robustly Optimized BERT Pretraining Approach."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_roberta_fact() -> dict:
"""Fetch the verified SourceScore claim for RoBERTa."""
r = httpx.get("https://sourcescore.org/api/v1/claims/d4fecb26a4c9cdca.json")
return r.json()