Verified claim · AI-ML · 100% confidence
Chinchilla scaling laws introduced in paper: Training Compute-Optimal Large Language Models (Hoffmann et al., 2022).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 8befcae6bce01a95
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Structured fields
- Subject
- Chinchilla scaling laws
- Predicate
introduced_in_paper- Object
- Training Compute-Optimal Large Language Models (Hoffmann et al., 2022)
- Confidence
- 100%
- Tags
- chinchilla · scaling-laws · foundational · hoffmann · 2022 · deepmind · nips
Sources (2)
[1] preprint · arXiv (Hoffmann et al., DeepMind) · 2022-03-29
Training Compute-Optimal Large Language Models“We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained.”
[2] peer reviewed · NeurIPS Foundation · 2022-12-06
Training Compute-Optimal Large Language Models (NeurIPS 2022)
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Evidence comes from 2 primary sources: arXiv (Hoffmann et al., DeepMind), NeurIPS Foundation. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/8befcae6bce01a95.json includes an HMAC-SHA256 signature for audit verification.
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// "Chinchilla scaling laws introduced in paper: Training Compute-Optimal Large Language Models (Hoffmann et al., 2022)."Python
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# "Chinchilla scaling laws introduced in paper: Training Compute-Optimal Large Language Models (Hoffmann et al., 2022)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_chinchilla_scaling_laws_fact() -> dict:
"""Fetch the verified SourceScore claim for Chinchilla scaling laws."""
r = httpx.get("https://sourcescore.org/api/v1/claims/8befcae6bce01a95.json")
return r.json()