RRepoGEO

REPOGEO REPORT · LITE

Weixin-Liang/LLM-scientific-feedback

Default branch main · commit 56bb700f · scanned 6/5/2026, 4:07:44 AM

GitHub: 531 stars · 52 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 Weixin-Liang/LLM-scientific-feedback, 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 the README's main heading and opening paragraph to highlight the practical application

    Why:

    CURRENT
    # Can large language models provide useful feedback on research papers? A large-scale empirical analysis.

[](https://www.python.org/downloads/release/python-3100/)
[](https://github.com/ambv/black)
[](https://arxiv.org/abs/2310.01783)

This repo provides the Python source code of our paper:
Can large language models provide useful feedback on research papers? A large-scale empirical analysis.
[[PDF]](https://arxiv.org/pdf/2310.01783.pdf)[[Twitter]](https://twitter.com/james_y_zou/status/1709608909395357946)
    COPY-PASTE FIX
    # LLM-Scientific-Feedback: Code and Resources for AI-Powered Research Paper Feedback

This repository provides the Python source code and empirical analysis from our paper, "Can large language models provide useful feedback on research papers? A large-scale empirical analysis." We also feature our "AI Feedback on Research Manuscripts" GPT, available on the OpenAI GPT store, enabling direct application of our research for comprehensive feedback on your manuscripts.

[[Paper PDF]](https://arxiv.org/pdf/2310.01783.pdf) [[Twitter]](https://twitter.com/james_y_zou/status/1709608909395357946) [[OpenAI GPT]](https://chat.openai.com/g/g-rqNGmiRU9-ai-feedback-on-research-manuscripts)
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, large-language-models, scientific-feedback, research-papers, peer-review, ai-feedback, nlp, academic-writing, gpt-store
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://chat.openai.com/g/g-rqNGmiRU9-ai-feedback-on-research-manuscripts

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 Weixin-Liang/LLM-scientific-feedback
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Grammarly
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Grammarly · recommended 2×
  2. DeepL Write · recommended 2×
  3. Trinka.ai · recommended 1×
  4. QuillBot · recommended 1×
  5. Writefull · recommended 1×
  • CATEGORY QUERY
    How can I leverage AI to obtain comprehensive feedback on my research manuscripts?
    you: not recommended
    AI recommended (in order):
    1. Trinka.ai
    2. QuillBot
    3. Grammarly
    4. DeepL Write
    5. Writefull
    6. Paperpal

    AI recommended 6 alternatives but never named Weixin-Liang/LLM-scientific-feedback. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for using LLMs to review and critique scientific papers?
    you: not recommended
    AI recommended (in order):
    1. ChatGPT
    2. Elicit
    3. Semantic Scholar
    4. Custom GPTs
    5. Anthropic's Claude
    6. Perplexity AI
    7. Scite.ai
    8. Connected Papers
    9. Grammarly
    10. DeepL Write

    AI recommended 10 alternatives but never named Weixin-Liang/LLM-scientific-feedback. 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 Weixin-Liang/LLM-scientific-feedback?
    pass
    AI named Weixin-Liang/LLM-scientific-feedback explicitly

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

  • If a team adopts Weixin-Liang/LLM-scientific-feedback in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name Weixin-Liang/LLM-scientific-feedback — 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?

  • In one sentence, what problem does the repo Weixin-Liang/LLM-scientific-feedback solve, and who is the primary audience?
    pass
    AI did not name Weixin-Liang/LLM-scientific-feedback — 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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Weixin-Liang/LLM-scientific-feedback — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite