RRepoGEO

REPOGEO REPORT · LITE

Zhen-Tan-dmml/LLM4Annotation

Default branch main · commit f54568f3 · scanned 6/1/2026, 5:42:55 PM

GitHub: 645 stars · 25 forks

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 Zhen-Tan-dmml/LLM4Annotation, 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
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A curated list of research papers and resources on leveraging Large Language Models (LLMs) for data annotation and synthesis, complementing our EMNLP 2024 survey.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, large-language-models, data-annotation, data-synthesis, survey, awesome-list, nlp, machine-learning, research-papers
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT License) in the root directory to clarify usage rights for the repository's structure and content.

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 Zhen-Tan-dmml/LLM4Annotation
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 2×
  2. Snorkel AI · recommended 2×
  3. Anthropic Claude · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. Google Gemini API · recommended 1×
  • CATEGORY QUERY
    How can I leverage large language models for efficient data labeling and synthesis?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Anthropic Claude
    3. Hugging Face Transformers
    4. Google Gemini API
    5. Snorkel AI
    6. Label Studio
    7. Microsoft Azure OpenAI Service

    AI recommended 7 alternatives but never named Zhen-Tan-dmml/LLM4Annotation. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best practices for evaluating and mitigating risks in LLM-generated data annotations?
    you: not recommended
    AI recommended (in order):
    1. Confluence
    2. Google Docs
    3. Label Studio (label-studio/label-studio)
    4. Argilla (argilla-io/argilla)
    5. Snorkel AI
    6. Python
    7. scikit-learn (scikit-learn/scikit-learn)
    8. NLTK (nltk/nltk)
    9. Pandas (pandas-dev/pandas)
    10. Deepchecks (deepchecks/deepchecks)
    11. Weights & Biases (W&B)
    12. Fairlearn (fairlearn/fairlearn)
    13. IBM AI Fairness 360 (AIF360) (IBM/AIF360)
    14. MLflow (mlflow/mlflow)
    15. DVC (Data Version Control) (iterative/dvc)
    16. OpenAI API
    17. Azure OpenAI Service
    18. Hugging Face Transformers (huggingface/transformers)

    AI recommended 18 alternatives but never named Zhen-Tan-dmml/LLM4Annotation. 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 Zhen-Tan-dmml/LLM4Annotation?
    pass
    AI did not name Zhen-Tan-dmml/LLM4Annotation — 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 Zhen-Tan-dmml/LLM4Annotation in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Zhen-Tan-dmml/LLM4Annotation 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 Zhen-Tan-dmml/LLM4Annotation solve, and who is the primary audience?
    pass
    AI named Zhen-Tan-dmml/LLM4Annotation 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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Zhen-Tan-dmml/LLM4Annotation — 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