Verified claim · AI-ML · 92% confidence
MATH dataset introduced in paper: Measuring Mathematical Problem Solving With the MATH Dataset (Hendrycks et al., 2021).
Last verified 2026-05-31 · Methodology veritas-v0.1 · 8c1f847ae98570da
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
Related verified claims
More verified claims related to this one — keep exploring.
GSM8K introduced in paper: Training Verifiers to Solve Math Word Problems (Cobbe et al., 2021).
92% confidence · shares 4 tags (benchmark, dataset, reasoning…)
MMLU benchmark introduced in paper: Measuring Massive Multitask Language Understanding (Hendrycks et al., 2020).
100% confidence · shares 2 tags (benchmark, hendrycks)
HumanEval benchmark introduced in paper: Evaluating Large Language Models Trained on Code (Chen et al., 2021).
100% confidence · shares 2 tags (benchmark, 2021)
ARC-AGI benchmark introduced in: Chollet 2019 — abstraction and reasoning corpus.
100% confidence · shares 2 tags (benchmark, reasoning)
GPQA benchmark introduced in paper: GPQA: A Graduate-Level Google-Proof Q&A Benchmark (Rein et al., 2023).
92% confidence · shares 2 tags (benchmark, reasoning)
TruthfulQA benchmark introduced in paper: TruthfulQA: Measuring How Models Mimic Human Falsehoods (Lin et al., 2021).
92% confidence · shares 2 tags (benchmark, 2021)
MMLU-Pro benchmark introduced in paper: MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark (Wang et al., 2024).
92% confidence · shares 2 tags (benchmark, reasoning)
Low-Rank Adaptation (LoRA) introduced in paper: LoRA: Low-Rank Adaptation of Large Language Models (Hu et al., 2021).
100% confidence · shares 1 tag (2021)
Structured fields
- Subject
- MATH dataset
- Predicate
introduced_in_paper- Object
- Measuring Mathematical Problem Solving With the MATH Dataset (Hendrycks et al., 2021)
- Confidence
- 92%
- Tags
- math-dataset · benchmark · dataset · mathematics · competition · reasoning · hendrycks · 2021
Sources (3)
[1] preprint · arXiv (Hendrycks, Burns, Kadavath, Arora, Basart, Tang, Song, Steinhardt) · 2021-03-05
Measuring Mathematical Problem Solving With the MATH Dataset“we introduce MATH, a new dataset of 12,500 challenging competition mathematics problems.”
[2] github release · Dan Hendrycks (hendrycks) · 2021-03-05
MATH dataset repository[3] docs · Hugging Face
MATH dataset paper (Hugging Face Papers)Hugging Face is rated by SourceScore — see its reliability →
Cite this claim
Ready-to-paste citation (Markdown / plain text):
MATH dataset introduced in paper: Measuring Mathematical Problem Solving With the MATH Dataset (Hendrycks et al., 2021). — SourceScore Claim 8c1f847ae98570da (verified 2026-05-31). https://sourcescore.org/api/v1/claims/8c1f847ae98570da.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the claim record + top cited source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/8c1f847ae98570da/" width="100%" height="360" frameborder="0" loading="lazy" title="MATH dataset introduced in paper: Measuring Mathematical Problem Solving With the MATH Dataset (Hendrycks et al., 2021)."></iframe>Preview: open in new tab
Frequently asked questions
How has SourceScore reviewed the claim "MATH dataset introduced in paper: Measuring Mathematical Problem Solving With the MATH Dataset (Hendrycks et al., 2021)."?
SourceScore records this assertion with 92% legacy editorial confidence as of 2026-05-31, under methodology veritas-v0.1. That metadata is not a truth guarantee. Inspect the 3 cited source record(s), excerpts, and live evidence below.
What is the evidence for "MATH dataset introduced in paper: Measuring Mathematical Problem Solving With the MATH Dataset (Hendrycks et al., 2021)."?
The record lists 3 cited source(s): arXiv (Hendrycks, Burns, Kadavath, Arora, Basart, Tang, Song, Steinhardt), Dan Hendrycks (hendrycks), Hugging Face. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/8c1f847ae98570da.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.
When was this claim record last reviewed by SourceScore?
Last reviewed 2026-05-31 under methodology version veritas-v0.1. The dated JSON record includes SourceScore-issued HMAC metadata, which is not publicly recomputable. Refetch the record and inspect current evidence before relying on it.
How can I cite this SourceScore claim in my code or article?
Fetch the JSON record from https://sourcescore.org/api/v1/claims/8c1f847ae98570da.json, including the verbatim claim, cited evidence, confidence, methodology version, and last-verified date. Refetch the canonical HTTPS record and inspect cited evidence; the HMAC tag is not publicly independently verifiable. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this record from your application. The response includes verbatim excerpts, primary-source URLs, and SourceScore-issued HMAC integrity metadata. Refetch the canonical HTTPS record and inspect cited evidence; public users cannot independently verify the HMAC tag.
cURL
curl https://sourcescore.org/api/v1/claims/8c1f847ae98570da.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/8c1f847ae98570da.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "MATH dataset introduced in paper: Measuring Mathematical Problem Solving With the MATH Dataset (Hendrycks et al., 2021)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/8c1f847ae98570da.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "MATH dataset introduced in paper: Measuring Mathematical Problem Solving With the MATH Dataset (Hendrycks et al., 2021)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_math_dataset_fact() -> dict:
"""Fetch the verified SourceScore claim for MATH dataset."""
r = httpx.get("https://sourcescore.org/api/v1/claims/8c1f847ae98570da.json")
return r.json()