Verified claim · AI-ML · 100% confidence
PaLM introduced in paper: PaLM: Scaling Language Modeling with Pathways (Chowdhery et al., 2022).
Last verified 2026-05-16 · Methodology veritas-v0.1 · d58d505fd9d705fe
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
Structured fields
- Subject
- PaLM
- Predicate
introduced_in_paper- Object
- PaLM: Scaling Language Modeling with Pathways (Chowdhery et al., 2022)
- Confidence
- 100%
- Tags
- palm · google · pathways · foundational · 2022 · parameter-count · 540b
Sources (2)
[1] preprint · arXiv (Chowdhery, Narang, Devlin, Bosma, Mishra, Roberts, et al.) · 2022-04-05
PaLM: Scaling Language Modeling with Pathways“We trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. … PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks.”
[2] official blog · Google Research · 2022-04-04
Pathways Language Model (PaLM): Scaling to 540 Billion Parameters
Cite this claim
Ready-to-paste citation (Markdown / plain text):
PaLM introduced in paper: PaLM: Scaling Language Modeling with Pathways (Chowdhery et al., 2022). — SourceScore Claim d58d505fd9d705fe (verified 2026-05-16). https://sourcescore.org/api/v1/claims/d58d505fd9d705fe.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the signed claim + primary source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/d58d505fd9d705fe/" width="100%" height="360" frameborder="0" loading="lazy" title="PaLM introduced in paper: PaLM: Scaling Language Modeling with Pathways (Chowdhery et al., 2022)."></iframe>Preview: open in new tab
Related claims
Other verified claims sharing tags with this one — useful for LLM retrieval graphs and citation discovery.
Chain-of-Thought prompting introduced in paper: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022).
3af924da138ff84c · 100% confidence · shares 3 tags (foundational, 2022, google)
Imagen introduced in paper: Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (Saharia et al., 2022).
30fdfa95f8684ca5 · 100% confidence · shares 3 tags (google, foundational, 2022)
Speculative decoding introduced in: Leviathan, Kalman, Matias 2023 — Google Research.
6cdc7730bf41bb3d · 100% confidence · shares 3 tags (google, foundational, 2022)
BERT (Bidirectional Encoder Representations from Transformers) introduced in paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., 2018).
4c1ee70007dc89c1 · 100% confidence · shares 2 tags (foundational, google)
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).
ef28341c3b308737 · 100% confidence · shares 2 tags (foundational, google)
Frequently asked questions
Is the claim "PaLM introduced in paper: PaLM: Scaling Language Modeling with Pathways (Chowdhery et al., 2022)." verified?
Yes — SourceScore verified this claim with 100% confidence as of 2026-05-16. 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 "PaLM introduced in paper: PaLM: Scaling Language Modeling with Pathways (Chowdhery et al., 2022)."?
Evidence comes from 2 primary sources: arXiv (Chowdhery, Narang, Devlin, Bosma, Mishra, Roberts, et al.), Google Research. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/d58d505fd9d705fe.json includes an HMAC-SHA256 signature for audit verification.
When was this claim last verified by SourceScore?
Last verified 2026-05-16 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/d58d505fd9d705fe.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.
Use this claim in your code
Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.
cURL
curl https://sourcescore.org/api/v1/claims/d58d505fd9d705fe.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/d58d505fd9d705fe.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "PaLM introduced in paper: PaLM: Scaling Language Modeling with Pathways (Chowdhery et al., 2022)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/d58d505fd9d705fe.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "PaLM introduced in paper: PaLM: Scaling Language Modeling with Pathways (Chowdhery et al., 2022)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_palm_fact() -> dict:
"""Fetch the verified SourceScore claim for PaLM."""
r = httpx.get("https://sourcescore.org/api/v1/claims/d58d505fd9d705fe.json")
return r.json()