RRepoGEO

REPOGEO REPORT · LITE

RUC-NLPIR/LLM4IR-Survey

Default branch main · commit bb39608f · scanned 6/11/2026, 9:22:57 PM

GitHub: 538 stars · 44 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 RUC-NLPIR/LLM4IR-Survey, 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 H1 and first sentence to emphasize official, comprehensive, and updated status

    Why:

    CURRENT
    # LLM4IR-Survey
This is the collection of papers related to large language models for information retrieval. These papers are organized according to our survey paper Large Language Models for Information Retrieval: A Survey.
    COPY-PASTE FIX
    # LLM4IR-Survey: The Official Collection of Papers for "Large Language Models for Information Retrieval: A Survey"
This repository serves as the definitive and regularly updated collection of papers related to large language models for information retrieval, meticulously organized to accompany our comprehensive survey paper.
  • highabout#2
    Update the repository description to be more specific and assertive

    Why:

    CURRENT
    This is the repo for the survey of LLM4IR.
    COPY-PASTE FIX
    The official repository for 'Large Language Models for Information Retrieval: A Survey', providing a comprehensive, regularly updated collection of papers and resources.
  • mediumtopics#3
    Add more specific topics to enhance categorization

    Why:

    CURRENT
    information-retrieval, large-language-models, survey
    COPY-PASTE FIX
    information-retrieval, large-language-models, survey, llm-for-ir, research-papers, literature-review, academic-survey, paper-collection

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 RUC-NLPIR/LLM4IR-Survey
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv.org
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv.org · recommended 1×
  2. ACL Anthology · recommended 1×
  3. SIGIR Conference Proceedings · recommended 1×
  4. NeurIPS · recommended 1×
  5. ICML · recommended 1×
  • CATEGORY QUERY
    I need to survey recent research on large language models for information retrieval.
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. ACL Anthology
    3. SIGIR Conference Proceedings
    4. NeurIPS
    5. ICML
    6. ICLR Proceedings
    7. GitHub
    8. Google Scholar
    9. Semantic Scholar

    AI recommended 9 alternatives but never named RUC-NLPIR/LLM4IR-Survey. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive overview of large language models applied to information retrieval?
    you: not recommended
    AI recommended (in order):
    1. Large Language Models for Information Retrieval: A Survey
    2. Retrieval-Augmented Generation (RAG) Papers
    3. A Survey on Large Language Models for Information Retrieval: A New Paradigm
    4. Information Retrieval Meets Large Language Models: A Survey
    5. Hugging Face
    6. Deep Learning for Information Retrieval

    AI recommended 6 alternatives but never named RUC-NLPIR/LLM4IR-Survey. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 RUC-NLPIR/LLM4IR-Survey?
    pass
    AI named RUC-NLPIR/LLM4IR-Survey explicitly

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

  • If a team adopts RUC-NLPIR/LLM4IR-Survey in production, what risks or prerequisites should they evaluate first?
    pass
    AI named RUC-NLPIR/LLM4IR-Survey 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 RUC-NLPIR/LLM4IR-Survey solve, and who is the primary audience?
    pass
    AI did not name RUC-NLPIR/LLM4IR-Survey — 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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  • Prioritized action items8 vs 3 in Lite