RRepoGEO

REPOGEO REPORT · LITE

leobeeson/llm_benchmarks

Default branch master · commit 53a8bcfe · scanned 6/1/2026, 12:42:47 AM

GitHub: 569 stars · 35 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 leobeeson/llm_benchmarks, 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:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm, benchmarks, evaluation, datasets, large-language-models, nlp, machine-learning, ai-evaluation, llm-benchmarking
  • highlicense#2
    Add a standard open-source 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 with the text of a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • highreadme#3
    Emphasize the unique value proposition in the README's opening

    Why:

    CURRENT
    # llm_benchmarks
A collection of benchmarks and datasets for evaluating LLM.
    COPY-PASTE FIX
    # llm_benchmarks
A lightweight and extensible collection of benchmarks and datasets for evaluating Large Language Models (LLMs), designed for researchers and developers seeking direct control over custom LLM benchmarking.

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 leobeeson/llm_benchmarks
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. OpenAI Evals · recommended 1×
  3. Hugging Face Evaluate Library · recommended 1×
  4. Big Bench · recommended 1×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    How to benchmark large language models across diverse knowledge and reasoning tasks?
    you: not recommended
    AI recommended (in order):
    1. EleutherAI/lm-evaluation-harness (EleutherAI/lm-evaluation-harness)
    2. OpenAI Evals
    3. Hugging Face Evaluate Library
    4. Big Bench
    5. LangChain
    6. Ragas
    7. DeepEval

    AI recommended 7 alternatives but never named leobeeson/llm_benchmarks. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What datasets are available for evaluating a large language model's general understanding?
    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. HellaSwag
    5. ARC (AI2 Reasoning Challenge)
    6. BoolQ
    7. SQuAD (Stanford Question Answering Dataset)

    AI recommended 7 alternatives but never named leobeeson/llm_benchmarks. 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 leobeeson/llm_benchmarks?
    pass
    AI named leobeeson/llm_benchmarks explicitly

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

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

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

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leobeeson/llm_benchmarks — 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