RRepoGEO

REPOGEO REPORT · LITE

quqxui/Awesome-LLM4IE-Papers

Default branch main · commit ee1db165 · scanned 5/11/2026, 2:38:17 PM

GitHub: 1,058 stars · 62 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 quqxui/Awesome-LLM4IE-Papers, 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
    Clarify the repository's nature as a curated list/survey in the README's opening

    Why:

    CURRENT
    # Awesome-LLM4IE-Papers
    COPY-PASTE FIX
    # Awesome-LLM4IE-Papers: A Curated List and Survey of Research on LLMs for Generative Information Extraction
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT, Apache-2.0) to the repository root to clearly state the terms of use for the content.
  • mediumabout#3
    Refine the 'About' description for clarity on content type

    Why:

    CURRENT
    Awesome papers about generative Information Extraction (IE) using Large Language Models (LLMs)
    COPY-PASTE FIX
    A comprehensive, curated collection of awesome research papers and a survey on generative Information Extraction (IE) using Large Language Models (LLMs).

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 quqxui/Awesome-LLM4IE-Papers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 1×
  2. Anthropic Claude · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. Google Cloud Vertex AI · recommended 1×
  5. Microsoft Azure OpenAI Service · recommended 1×
  • CATEGORY QUERY
    How can I leverage large language models for effective information extraction tasks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Anthropic Claude
    3. Hugging Face Transformers
    4. Google Cloud Vertex AI
    5. Microsoft Azure OpenAI Service
    6. spaCy
    7. Haystack

    AI recommended 7 alternatives but never named quqxui/Awesome-LLM4IE-Papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best approaches for zero-shot or few-shot information extraction with LLMs?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4 / GPT-3.5 Turbo
    2. Anthropic Claude 3 (Opus/Sonnet/Haiku)
    3. Google Gemini (Advanced/Pro)
    4. Hugging Face Transformers Library (huggingface/transformers)
    5. OpenAI Fine-tuning API
    6. LangChain (langchain-ai/langchain)
    7. LlamaIndex (run-llama/llama_index)
    8. Pinecone
    9. Weaviate (weaviate/weaviate)
    10. Chroma (chroma-core/chroma)
    11. Elasticsearch (elastic/elasticsearch)
    12. Guidance (Microsoft) (microsoft/guidance)
    13. BioBERT (dmis-lab/biobert)
    14. ClinicalBERT (yikuan8/ClinicalBERT)
    15. LegalBERT (Legal-AI/LegalBERT)
    16. SciBERT (allenai/scibert)

    AI recommended 16 alternatives but never named quqxui/Awesome-LLM4IE-Papers. 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 quqxui/Awesome-LLM4IE-Papers?
    pass
    AI did not name quqxui/Awesome-LLM4IE-Papers — 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 quqxui/Awesome-LLM4IE-Papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named quqxui/Awesome-LLM4IE-Papers 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 quqxui/Awesome-LLM4IE-Papers solve, and who is the primary audience?
    pass
    AI did not name quqxui/Awesome-LLM4IE-Papers — 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

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MARKDOWN (README)
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quqxui/Awesome-LLM4IE-Papers — 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