RRepoGEO

REPOGEO REPORT · LITE

hendrycks/test

Default branch master · commit 4450500f · scanned 6/27/2026, 3:17:45 PM

GitHub: 1,592 stars · 117 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 hendrycks/test, 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
  • highreadme#1
    Explicitly state that this repository is the MMLU benchmark in the README title and opening paragraph

    Why:

    CURRENT
    # Measuring Massive Multitask Language Understanding
This is the repository for Measuring Massive Multitask Language Understanding by
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).
    COPY-PASTE FIX
    # MMLU: Measuring Massive Multitask Language Understanding
This is the official repository for the Massive Multitask Language Understanding (MMLU) benchmark, introduced in the ICLR 2021 paper 'Measuring Massive Multitask Language Understanding' by Dan Hendrycks et al.
  • mediumtopics#2
    Add more specific topics related to LLM evaluation and benchmarks

    Why:

    CURRENT
    few-shot-learning, gpt-3, muti-task, transfer-learning
    COPY-PASTE FIX
    few-shot-learning, gpt-3, muti-task, transfer-learning, large-language-models, llm-evaluation, benchmark, natural-language-understanding, mmlu
  • lowreadme#3
    Add a concise problem statement to the README's opening paragraph

    Why:

    COPY-PASTE FIX
    MMLU provides a comprehensive evaluation of general knowledge and reasoning abilities for large language models across 57 academic and professional subjects.

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 hendrycks/test
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MMLU
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MMLU · recommended 1×
  2. HELM · recommended 1×
  3. ARC · recommended 1×
  4. HellaSwag · recommended 1×
  5. BIG-bench · recommended 1×
  • CATEGORY QUERY
    How can I evaluate a large language model's understanding across many different academic subjects?
    you: not recommended
    AI recommended (in order):
    1. MMLU
    2. HELM
    3. ARC
    4. HellaSwag
    5. BIG-bench

    AI recommended 5 alternatives but never named hendrycks/test. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a benchmark dataset to test few-shot learning and transfer capabilities of new AI models.
    you: not recommended
    AI recommended (in order):
    1. Meta-Dataset
    2. miniImageNet
    3. tieredImageNet
    4. Omniglot
    5. FewShot-CIFAR100
    6. Cross-Domain Few-Shot Learning (CD-FSL) Benchmark
    7. FS-COCO (Few-Shot COCO)
    8. GLUE
    9. SuperGLUE
    10. CLINC150
    11. ATIS

    AI recommended 11 alternatives but never named hendrycks/test. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 hendrycks/test?
    pass
    AI named hendrycks/test explicitly

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

  • If a team adopts hendrycks/test in production, what risks or prerequisites should they evaluate first?
    pass
    AI named hendrycks/test 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 hendrycks/test solve, and who is the primary audience?
    pass
    AI did not name hendrycks/test — 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?

Embed your GEO score

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hendrycks/test — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
hendrycks/test — RepoGEO report