Verified claim · AI-ML · 100% confidence
MMLU benchmark introduced in paper: Measuring Massive Multitask Language Understanding (Hendrycks et al., 2020).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 428d754e7c651be6
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Structured fields
- Subject
- MMLU benchmark
- Predicate
introduced_in_paper- Object
- Measuring Massive Multitask Language Understanding (Hendrycks et al., 2020)
- Confidence
- 100%
- Tags
- mmlu · benchmark · hendrycks · 2020 · iclr · evaluation
Sources (2)
[1] preprint · arXiv (Hendrycks et al.) · 2020-09-07
Measuring Massive Multitask Language Understanding“We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more.”
[2] peer reviewed · OpenReview / ICLR · 2021-05-04
Measuring Massive Multitask Language Understanding (ICLR 2021)
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MMLU benchmark introduced in paper: Measuring Massive Multitask Language Understanding (Hendrycks et al., 2020). — SourceScore Claim 428d754e7c651be6 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/428d754e7c651be6.jsonEmbed this claim
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curl https://sourcescore.org/api/v1/claims/428d754e7c651be6.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/428d754e7c651be6.json");
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// "MMLU benchmark introduced in paper: Measuring Massive Multitask Language Understanding (Hendrycks et al., 2020)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/428d754e7c651be6.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "MMLU benchmark introduced in paper: Measuring Massive Multitask Language Understanding (Hendrycks et al., 2020)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_mmlu_benchmark_fact() -> dict:
"""Fetch the verified SourceScore claim for MMLU benchmark."""
r = httpx.get("https://sourcescore.org/api/v1/claims/428d754e7c651be6.json")
return r.json()