RRepoGEO

REPOGEO REPORT · LITE

google/BIG-bench

Default branch main · commit 092b196c · scanned 5/16/2026, 3:02:40 PM

GitHub: 3,239 stars · 617 forks

AI VISIBILITY SCORE
35 /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
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 google/BIG-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 improve categorization

    Why:

    COPY-PASTE FIX
    large-language-models, llm, benchmark, evaluation, nlp, artificial-intelligence, machine-learning, research, google
  • mediumreadme#2
    Clarify the README's opening sentence for better keyword alignment

    Why:

    CURRENT
    The Beyond the Imitation Game Benchmark (BIG-bench) is a *collaborative* benchmark intended to probe large language models and extrapolate their future capabilities.
    COPY-PASTE FIX
    The Beyond the Imitation Game Benchmark (BIG-bench) is a *collaborative* benchmark for AI researchers and developers, designed to comprehensively evaluate and probe the capabilities of large language models (LLMs) across a wide range of tasks.
  • lowhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/google/BIG-bench

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 google/BIG-bench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
HumanEval
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. HumanEval · recommended 2×
  2. EleutherAI/lm-evaluation-harness · recommended 1×
  3. OpenAI Evals · recommended 1×
  4. LangChain · recommended 1×
  5. Ragas · recommended 1×
  • CATEGORY QUERY
    How can I comprehensively benchmark and evaluate the capabilities of large language models?
    you: not recommended
    AI recommended (in order):
    1. EleutherAI/lm-evaluation-harness (EleutherAI/lm-evaluation-harness)
    2. OpenAI Evals
    3. LangChain
    4. Ragas
    5. HumanEval
    6. MBPP
    7. CodeXGLUE
    8. HELM
    9. Hugging Face Datasets
    10. Hugging Face Transformers

    AI recommended 10 alternatives but never named google/BIG-bench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some robust benchmarks for assessing various tasks in large language model development?
    you: not recommended
    AI recommended (in order):
    1. GLUE (General Language Understanding Evaluation) Benchmark
    2. SuperGLUE (Super General Language Understanding Evaluation) Benchmark
    3. MMLU (Massive Multitask Language Understanding)
    4. HELM (Holistic Evaluation of Language Models)
    5. BIG-bench (Beyond the Imitation Game Benchmark)
    6. HumanEval
    7. TruthfulQA

    AI recommended 7 alternatives but never named google/BIG-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 google/BIG-bench?
    pass
    AI named google/BIG-bench explicitly

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

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

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

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google/BIG-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