RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/llm-transparency-tool

Default branch main · commit f1340f07 · scanned 5/12/2026, 2:57:42 PM

GitHub: 1,253 stars · 107 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 facebookresearch/llm-transparency-tool, 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 specific topics for LLM interpretability and analysis

    Why:

    COPY-PASTE FIX
    llm-interpretability, llm-analysis, transformer-models, language-models, deep-learning, ai-transparency, model-debugging, attention-mechanisms
  • highreadme#2
    Strengthen README's opening to clarify core purpose

    Why:

    CURRENT
    <h1>
  
</h1>

## Key functionality
    COPY-PASTE FIX
    The LLM Transparency Tool (LLM-TT) is an open-source interactive toolkit designed for deep analysis of the internal workings of Transformer-based language models, focusing on token contributions, attention mechanisms, and neuron activations.

## Key functionality
  • highhomepage#3
    Add the demo link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://huggingface.co/spaces/facebook/llm-transparency-tool-demo

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 facebookresearch/llm-transparency-tool
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Captum
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Captum · recommended 1×
  2. LIME · recommended 1×
  3. SHAP · recommended 1×
  4. exBERT · recommended 1×
  5. BERTViz · recommended 1×
  • CATEGORY QUERY
    How can I visualize and understand the internal workings of a large language model?
    you: not recommended
    AI recommended (in order):
    1. Captum
    2. LIME
    3. SHAP
    4. exBERT
    5. BERTViz
    6. Neuroscope
    7. Language Interpretability Tool (LIT)
    8. TCAV
    9. Hugging Face Transformers

    AI recommended 9 alternatives but never named facebookresearch/llm-transparency-tool. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools to analyze token contributions and attention mechanisms within LLMs.
    you: not recommended
    AI recommended (in order):
    1. Captum (pytorch/captum)
    2. LIME (marcotcr/lime)
    3. SHAP (shap/shap)
    4. Hugging Face Transformers Library (huggingface/transformers)
    5. BertViz (jessevig/bertviz)
    6. ExBERT (exbert-project/exbert)
    7. TensorBoard (tensorflow/tensorboard)
    8. Weights & Biases (W&B) (wandb/wandb)
    9. Matplotlib (matplotlib/matplotlib)
    10. Seaborn (mwaskom/seaborn)

    AI recommended 10 alternatives but never named facebookresearch/llm-transparency-tool. 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 facebookresearch/llm-transparency-tool?
    pass
    AI named facebookresearch/llm-transparency-tool explicitly

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

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

Embed your GEO score

Drop this badge into the README of facebookresearch/llm-transparency-tool. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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facebookresearch/llm-transparency-tool — 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