RRepoGEO

REPOGEO REPORT · LITE

MLGroupJLU/LLM-eval-survey

Default branch main · commit 40f44fd9 · scanned 5/27/2026, 2:27:51 AM

GitHub: 1,601 stars · 99 forks

AI VISIBILITY SCORE
28 /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
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 MLGroupJLU/LLM-eval-survey, 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
    Clarify the README's opening sentence to emphasize "survey" and "literature review"

    Why:

    CURRENT
    A collection of papers and resources related to evaluations on large language models.
    COPY-PASTE FIX
    This repository is the official, continuously updated collection of papers and resources for "A Survey on Evaluation of Large Language Models," serving as a comprehensive literature review, not an executable tool or platform.
  • highlicense#2
    Add a 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, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, or CC-BY-4.0 for content).
  • mediumtopics#3
    Add "survey" and "literature-review" to the repository topics

    Why:

    CURRENT
    benchmark, evaluation, large-language-models, llm, llms, model-assessment
    COPY-PASTE FIX
    benchmark, evaluation, large-language-models, llm, llms, model-assessment, survey, literature-review

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 MLGroupJLU/LLM-eval-survey
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Evals
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Evals · recommended 2×
  2. LangChain · recommended 1×
  3. Ragas · recommended 1×
  4. DeepEval · recommended 1×
  5. Arize AI (Phoenix) · recommended 1×
  • CATEGORY QUERY
    What are the best practices and resources for evaluating large language models?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Ragas
    3. DeepEval
    4. Arize AI (Phoenix)
    5. Humanloop
    6. OpenAI Evals
    7. Hugging Face Evaluate Library

    AI recommended 7 alternatives but never named MLGroupJLU/LLM-eval-survey. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive overview of LLM evaluation benchmarks and model assessment techniques?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Leaderboard
    2. Papers With Code
    3. Stanford HELM
    4. OpenAI Evals
    5. EleutherAI's LM Evaluation Harness
    6. Awesome-LLM-Evaluation

    AI recommended 6 alternatives but never named MLGroupJLU/LLM-eval-survey. 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 MLGroupJLU/LLM-eval-survey?
    pass
    AI named MLGroupJLU/LLM-eval-survey explicitly

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

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

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MLGroupJLU/LLM-eval-survey — RepoGEO report