Verified claim · AI-ML · 92% confidence
MMLU-Pro benchmark introduced in paper: MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark (Wang et al., 2024).
Last verified 2026-05-31 · Methodology veritas-v0.1 · 2df92e0b0e4c891b
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Structured fields
- Subject
- MMLU-Pro benchmark
- Predicate
introduced_in_paper- Object
- MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark (Wang et al., 2024)
- Confidence
- 92%
- Tags
- mmlu-pro · benchmark · evaluation · reasoning · wang · 2024
Sources (3)
[1] preprint · arXiv (Yubo Wang et al. — TIGER-Lab) · 2024-06-03
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark“This paper introduces MMLU-Pro, an enhanced dataset designed to extend the mostly knowledge-driven MMLU benchmark by integrating more challenging, reasoning-focused questions”
[2] github release · TIGER-AI-Lab · 2024-06-03
MMLU-Pro official repository (NeurIPS 2024)[3] model card · Hugging Face
MMLU-Pro dataset cardHugging Face is rated by SourceScore — see its reliability →
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const r = await fetch("https://sourcescore.org/api/v1/claims/2df92e0b0e4c891b.json");
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// "MMLU-Pro benchmark introduced in paper: MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark (Wang et al., 2024)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/2df92e0b0e4c891b.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "MMLU-Pro benchmark introduced in paper: MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark (Wang et al., 2024)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_mmlu_pro_benchmark_fact() -> dict:
"""Fetch the verified SourceScore claim for MMLU-Pro benchmark."""
r = httpx.get("https://sourcescore.org/api/v1/claims/2df92e0b0e4c891b.json")
return r.json()