RRepoGEO

REPOGEO REPORT · LITE

haonan-li/CMMLU

Default branch master · commit d6e7b716 · scanned 5/16/2026, 2:02:24 AM

GitHub: 815 stars · 68 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface haonan-li/CMMLU, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    chinese-nlp, llm-evaluation, benchmark, language-models, multitask-learning, nlp, chinese-language, ai-benchmark, large-language-models, reasoning, knowledge-assessment, chinese-culture
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root, for example, using the MIT License template, to clearly state the terms of use.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2306.09212

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

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface haonan-li/CMMLU
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
C-Eval
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. C-Eval · recommended 2×
  2. CMMLU (Chinese Massive Multitask Language Understanding) · recommended 2×
  3. CLUE (Chinese Language Understanding Evaluation) Benchmark · recommended 2×
  4. GAOKAO-Bench · recommended 1×
  5. ChaiNLI (Chinese Natural Language Inference) · recommended 1×
  • CATEGORY QUERY
    How can I evaluate large language models specifically for Chinese language comprehension and reasoning?
    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. ChaiNLI (Chinese Natural Language Inference)
    6. COPA-Zh (Choice of Plausible Alternatives - Chinese)
    7. WMT (Workshop on Machine Translation) Chinese-English/English-Chinese Translation Tasks

    AI recommended 7 alternatives but never named haonan-li/CMMLU. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best benchmarks for assessing a language model's understanding of Chinese cultural context?
    you: not recommended
    AI recommended (in order):
    1. C-Eval
    2. CMMLU (Chinese Massive Multitask Language Understanding)
    3. CLUE (Chinese Language Understanding Evaluation) Benchmark
    4. COPA-ZH (Choice of Plausible Alternatives - Chinese)
    5. ChnSentiCorp
    6. Chinese Medical Dialogues (CMD)

    AI recommended 6 alternatives but never named haonan-li/CMMLU. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of haonan-li/CMMLU?
    pass
    AI named haonan-li/CMMLU explicitly

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

  • If a team adopts haonan-li/CMMLU in production, what risks or prerequisites should they evaluate first?
    pass
    AI named haonan-li/CMMLU 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 haonan-li/CMMLU solve, and who is the primary audience?
    pass
    AI named haonan-li/CMMLU explicitly

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

Embed your GEO score

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haonan-li/CMMLU — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite