RRepoGEO

REPOGEO REPORT · LITE

thunlp/PromptPapers

Default branch main · commit 1ae4bd1e · scanned 6/24/2026, 6:12:45 PM

GitHub: 4,317 stars · 389 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)

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

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/PromptPapers, 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
    Reposition README's opening to clearly state its purpose

    Why:

    CURRENT
    # PromptPapers

   

We have released an open-source prompt-learning toolkit, check out **OpenPrompt!**
    COPY-PASTE FIX
    # PromptPapers: A Curated List of Essential Papers on Prompt-Based Tuning for LLMs

This repository provides a comprehensive and actively maintained collection of must-read research papers on prompt-based tuning for pre-trained language models. It is designed for researchers and practitioners seeking to understand and apply prompt engineering in NLP.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text of a permissive open-source license, such as MIT License, to clearly define usage rights for the paper list and any associated code.
  • mediumtopics#3
    Refine repository topics for accuracy and specificity

    Why:

    CURRENT
    ai, bert, machine-learning, nlp, pre-trained-language-models, prompt, prompt-based, prompt-learning, prompt-toolkit
    COPY-PASTE FIX
    ai, bert, machine-learning, nlp, pre-trained-language-models, prompt-engineering, prompt-tuning, large-language-models, research-papers, awesome-list

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/PromptPapers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome-Prompt-Engineering GitHub Repository
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome-Prompt-Engineering GitHub Repository · recommended 1×
  2. Papers With Code · recommended 1×
  3. arXiv · recommended 1×
  4. Google Scholar · recommended 1×
  5. Semantic Scholar · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of research papers on prompt-based tuning for LLMs?
    you: not recommended
    AI recommended (in order):
    1. Awesome-Prompt-Engineering GitHub Repository
    2. Papers With Code
    3. arXiv
    4. Google Scholar
    5. Semantic Scholar
    6. ACL Anthology
    7. OpenReview

    AI recommended 7 alternatives but never named thunlp/PromptPapers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the essential papers to understand prompt engineering for natural language processing models?
    you: not recommended
    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/PromptPapers?
    pass
    AI did not name thunlp/PromptPapers — 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/PromptPapers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named thunlp/PromptPapers 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/PromptPapers solve, and who is the primary audience?
    pass
    AI did not name thunlp/PromptPapers — 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/PromptPapers — 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