RRepoGEO

REPOGEO REPORT · LITE

llm2014/llm_benchmark

Default branch main · commit 5f0da23b · scanned 5/22/2026, 8:43:14 AM

GitHub: 1,149 stars · 9 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)

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

AI VISIBILITY SCORE
23 /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
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 llm2014/llm_benchmark, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify repo's modern LLM focus and address '2014' in name

    Why:

    CURRENT
    # 大模型测评记录
    COPY-PASTE FIX
    # llm2014/llm_benchmark: 现代大模型测评记录 (Modern LLM Benchmark Record)

本评测专注于评估和跟踪当前主流大型语言模型（LLMs）在逻辑、数学、编程和人类直觉等方面的长期进化趋势。请注意，'llm2014'是仓库名称的一部分，并非指项目创建于2014年。
  • highabout#2
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    个人性质的现代大语言模型（LLM）长期跟踪评测，侧重逻辑、数学、编程和人类直觉等复杂能力。

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 llm2014/llm_benchmark
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. BIG-bench · recommended 2×
  3. HumanEval · recommended 2×
  4. HELM · recommended 2×
  5. GSM8K · recommended 1×
  • CATEGORY QUERY
    How to benchmark large language models for advanced reasoning and complex problem-solving abilities?
    you: not recommended
    AI recommended (in order):
    1. MMLU
    2. GSM8K
    3. BIG-bench
    4. ARC
    5. HumanEval
    6. MATH
    7. HELM

    AI recommended 7 alternatives but never named llm2014/llm_benchmark. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks exist for evaluating LLM instruction following, logic, and mathematical reasoning comprehensively?
    you: not recommended
    AI recommended (in order):
    1. HELM
    2. BIG-bench
    3. EleutherAI's LM Evaluation Harness
    4. OpenAI Evals
    5. MMLU
    6. HumanEval

    AI recommended 6 alternatives but never named llm2014/llm_benchmark. 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 llm2014/llm_benchmark?
    pass
    AI did not name llm2014/llm_benchmark — 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 llm2014/llm_benchmark in production, what risks or prerequisites should they evaluate first?
    pass
    AI named llm2014/llm_benchmark 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 llm2014/llm_benchmark solve, and who is the primary audience?
    pass
    AI named llm2014/llm_benchmark explicitly

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

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llm2014/llm_benchmark — 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