Verified65% confidenceBenchmarkExact time
The MMLU (Massive Multitask Language Understanding) benchmark contains more than 14,000 questions.
3
Sources
65%
Confidence
Long-term
Relevance
7/21/2026
First Seen
Sources
Related Claims
UnverifiedMMLU measures multitask language understanding, HumanEval measures code generation, and MATH measures mathematical reasoning.69% similarUnverifiedMainstream large language model evaluation systems such as MMLU, HellaSwag, and BIG-Bench primarily measure knowledge coverage, logical reasoning, and linguistic fluency, with the design assumption that there are verifiable correct answers.62% similarUnverified上下文窗口决定模型单次能处理的文本量：8K约6000词，32K约24000词，128K可处理整本书内容60% similarUnverifiedCommon public benchmarks such as MMLU focus on multi-subject question answering, HumanEval on code generation, and MATH on mathematical reasoning, each emphasizing different dimensions.59% similarUnverifiedVector databases use embedding models (such as OpenAI's text-embedding-3-large or the open-source BGE-M3) to convert text into floating-point vectors of hundreds to thousands of dimensions, where vector distance corresponds to semantic similarity.58% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/579839API
curl https://kongchang.com/api/v1/knowledge/claims/579839MCP
get_claim(id=579839)