Verified claim · AI-ML · 100% confidence
Kaplan scaling laws introduced in paper: Kaplan et al. 2020 — Scaling Laws for Neural Language Models.
Last verified 2026-05-16 · Methodology veritas-v0.1 · 22e12bfbe7770657
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Structured fields
- Subject
- Kaplan scaling laws
- Predicate
introduced_in_paper- Object
- Kaplan et al. 2020 — Scaling Laws for Neural Language Models
- Confidence
- 100%
- Tags
- kaplan-scaling-laws · scaling-laws · openai · kaplan · foundational · 2020 · introduced_in
Sources (2)
[1] preprint · arXiv (Kaplan, McCandlish, Henighan, Brown, Chess, Child, Gray, Radford, Wu, Amodei / OpenAI) · 2020-01-23
Scaling Laws for Neural Language Models“We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude.”
[2] github release · OpenAI · 2019-02-14
GPT-2 — OpenAI repository (predecessor of GPT-3 scaling tests)OpenAI is rated by SourceScore — see its reliability →
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// "Kaplan scaling laws introduced in paper: Kaplan et al. 2020 — Scaling Laws for Neural Language Models."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/22e12bfbe7770657.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Kaplan scaling laws introduced in paper: Kaplan et al. 2020 — Scaling Laws for Neural Language Models."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_kaplan_scaling_laws_fact() -> dict:
"""Fetch the verified SourceScore claim for Kaplan scaling laws."""
r = httpx.get("https://sourcescore.org/api/v1/claims/22e12bfbe7770657.json")
return r.json()