RRepoGEO

REPOGEO REPORT · LITE

thunlp/PLMpapers

Default branch master · commit 0416bc36 · scanned 5/25/2026, 8:22:49 AM

GitHub: 3,363 stars · 434 forks

AI VISIBILITY SCORE
22 /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
1 / 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 thunlp/PLMpapers, 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
    Reposition README H1 to emphasize 'curated list'

    Why:

    CURRENT
    # Must-Read Papers on Pre-trained Language Models (PLMs)
    COPY-PASTE FIX
    # Curated List of Must-Read Papers on Pre-trained Language Models (PLMs)
  • mediumhomepage#2
    Add a homepage link to the THUNLP group or related project

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://thunlp.github.io/

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 thunlp/PLMpapers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
openai/gpt-2
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. openai/gpt-2 · recommended 2×
  2. Awesome Pretrained Language Models · recommended 1×
  3. Hugging Face Blog/Research Section · recommended 1×
  4. Papers With Code · recommended 1×
  5. Stanford CS224N · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated list of essential research papers on pre-trained language models?
    you: not recommended
    AI recommended (in order):
    1. Awesome Pretrained Language Models
    2. Hugging Face Blog/Research Section
    3. Papers With Code
    4. Stanford CS224N
    5. A Survey of Large Language Models
    6. Google AI Blog

    AI recommended 6 alternatives but never named thunlp/PLMpapers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the most important academic papers for understanding large language model development?
    you: not recommended
    AI recommended (in order):
    1. BERT (tensorflow/models/tree/master/official/legacy/bert)
    2. GPT (openai/gpt-2)
    3. GPT-2 (openai/gpt-2)
    4. Chinchilla model
    5. LLaMA (facebookresearch/llama)
    6. ChatGPT

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