RRepoGEO

REPOGEO REPORT · LITE

hendrycks/test

Default branch master · commit 4450500f · scanned 5/16/2026, 5:50:58 PM

GitHub: 1,579 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
40 /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
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 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
    Prominently feature 'MMLU' in the README title

    Why:

    CURRENT
    # Measuring Massive Multitask Language Understanding
    COPY-PASTE FIX
    # MMLU: Measuring Massive Multitask Language Understanding
  • mediumtopics#2
    Add 'evaluation' and 'benchmark' related topics

    Why:

    CURRENT
    few-shot-learning, gpt-3, muti-task, transfer-learning
    COPY-PASTE FIX
    few-shot-learning, gpt-3, multi-task, transfer-learning, language-model-evaluation, llm-benchmark, nlp-benchmark
  • lowabout#3
    Include 'MMLU' and clarify purpose in the repository description

    Why:

    CURRENT
    Measuring Massive Multitask Language Understanding | ICLR 2021
    COPY-PASTE FIX
    MMLU: Measuring Massive Multitask Language Understanding, a benchmark for evaluating LLMs across diverse subjects | ICLR 2021

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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MMLU · recommended 2×
  2. huggingface/datasets · recommended 1×
  3. huggingface/evaluate · recommended 1×
  4. EleutherAI/lm-evaluation-harness · recommended 1×
  5. openai/evals · recommended 1×
  • CATEGORY QUERY
    How can I comprehensively evaluate the performance of large language models on many different subjects?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Datasets (huggingface/datasets)
    2. Hugging Face Evaluate (huggingface/evaluate)
    3. EleutherAI's LM Evaluation Harness (EleutherAI/lm-evaluation-harness)
    4. OpenAI Evals (openai/evals)
    5. MMLU
    6. HELM (stanford-crfm/helm)
    7. Big Bench (google/BIG-bench)
    8. Amazon Mechanical Turk
    9. Appen

    AI recommended 9 alternatives but never named hendrycks/test. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good benchmarks for comparing few-shot learning capabilities of different language models?
    you: not recommended
    AI recommended (in order):
    1. BIG-bench
    2. MMLU
    3. HELM
    4. SuperGLUE
    5. RAFT
    6. EleutherAI's LM Evaluation Harness
    7. XNLI

    AI recommended 7 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 named hendrycks/test 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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hendrycks/test — 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