RRepoGEO

REPOGEO REPORT · LITE

OpenLMLab/GAOKAO-Bench

Default branch main · commit 6dbb24f8 · scanned 6/6/2026, 5:32:44 AM

GitHub: 756 stars · 61 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 OpenLMLab/GAOKAO-Bench, 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
    llm-evaluation, llm-benchmark, gaokao, chinese-llm, language-models, nlp, reasoning, language-comprehension, education-ai
  • highreadme#2
    Reposition the README's opening sentence to highlight unique value

    Why:

    CURRENT
    GAOKAO-Bench is an evaluation framework that utilizes GAOKAO questions as a dataset to evaluate large language models.
    COPY-PASTE FIX
    GAOKAO-Bench is a unique and rigorous evaluation framework that leverages high-stakes Chinese Gaokao (National College Entrance Examination) questions to benchmark large language models' language understanding and logical reasoning abilities.
  • mediumhomepage#3
    Add the project's academic paper as the homepage URL

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2305.12474

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 OpenLMLab/GAOKAO-Bench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
EleutherAI/lm-evaluation-harness
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. EleutherAI/lm-evaluation-harness · recommended 1×
  2. huggingface/evaluate · recommended 1×
  3. argilla-io/argilla · recommended 1×
  4. Surveymonkey/Qualtrics · recommended 1×
  5. microsoft/checklist · recommended 1×
  • CATEGORY QUERY
    How can I rigorously evaluate large language models using high-stakes national exam questions?
    you: not recommended
    AI recommended (in order):
    1. EleutherAI's LM Evaluation Harness (lm-eval) (EleutherAI/lm-evaluation-harness)
    2. Hugging Face Evaluate Library (huggingface/evaluate)
    3. Argilla (argilla-io/argilla)
    4. Surveymonkey/Qualtrics
    5. CheckList (microsoft/checklist)
    6. TextAttack (TextAttack/TextAttack)
    7. spaCy/NLTK
    8. LangChain/LlamaIndex

    AI recommended 8 alternatives but never named OpenLMLab/GAOKAO-Bench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks exist for comprehensively benchmarking LLM logical reasoning and language comprehension abilities?
    you: not recommended
    AI recommended (in order):
    1. HELM
    2. BIG-bench
    3. MMLU
    4. ARC
    5. SuperGLUE
    6. GSM8K
    7. TruthfulQA

    AI recommended 7 alternatives but never named OpenLMLab/GAOKAO-Bench. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 OpenLMLab/GAOKAO-Bench?
    pass
    AI did not name OpenLMLab/GAOKAO-Bench — 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 OpenLMLab/GAOKAO-Bench in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenLMLab/GAOKAO-Bench 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 OpenLMLab/GAOKAO-Bench solve, and who is the primary audience?
    pass
    AI named OpenLMLab/GAOKAO-Bench 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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MARKDOWN (README)
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OpenLMLab/GAOKAO-Bench — 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
OpenLMLab/GAOKAO-Bench — RepoGEO report