hkust-nlp/ceval

• mediumreadme#1

  Strengthen README's opening to assert official status and unique value

  Why:

  CURRENT

  C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels, as shown below. Please visit our website or check our paper for more details.

  COPY-PASTE FIX

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  C-Eval is the **official and most comprehensive Chinese evaluation suite** for foundation models. As presented at NeurIPS 2023, it features 13948 multi-choice questions across 52 diverse disciplines and four difficulty levels, serving as the primary benchmark for Chinese LLMs. Visit our website or check our paper for more details.

  OpenDoneDismiss
• lowreadme#2

  Expand "Why C-Eval?" section to explicitly state differentiators

  Why:

  CURRENT

  📝 Why C-Eval? How did we build it? (in Chinese)

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  ## Why C-Eval? (Core Differentiators)
  
  C-Eval stands out as the leading benchmark for Chinese LLMs due to its:
  - **Comprehensiveness:** 13948 questions across 52 disciplines.
  - **Multi-level Difficulty:** Four distinct difficulty levels for nuanced evaluation.
  - **Official Status:** The primary reference for Chinese foundation model evaluation, accepted at NeurIPS 2023.
  - **Community Integration:** Widely adopted and integrated into evaluation harnesses like lm-evaluation-harness.

  OpenDoneDismiss

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How can I benchmark large language models specifically for Chinese language understanding?

  you: not recommended
  AI recommended (in order):

  1. C-Eval
  2. CMMLU (Chinese Massive Multitask Language Understanding)
  3. GAOKAO-Bench
  4. CLUE (Chinese Language Understanding Evaluation) Benchmark
  5. LongBench
  6. Xiezhi (獇貈)

  AI recommended 6 alternatives but never named hkust-nlp/ceval. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  Need a comprehensive, multi-disciplinary benchmark for evaluating Chinese large language models.

  you: not recommended
  AI recommended (in order):

  1. C-Eval
  2. CMMLU
  3. GAOKAO-Bench
  4. AGIEval
  5. CLUE
  6. Xiezhi

  AI recommended 6 alternatives but never named hkust-nlp/ceval. This is the gap to close.

  Show full AI answer

• Metadata completeness

  warn

  Suggestion:

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of hkust-nlp/ceval?

  pass
  ⚠AI did not name hkust-nlp/ceval — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts hkust-nlp/ceval in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named hkust-nlp/ceval explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo hkust-nlp/ceval solve, and who is the primary audience?

  pass
  ✓AI named hkust-nlp/ceval explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?