RRepoGEO

REPOGEO REPORT · LITE

cocacola-lab/ChatIE

Default branch main · commit fa818f41 · scanned 6/8/2026, 2:23:25 PM

GitHub: 825 stars · 68 forks

AI VISIBILITY SCORE
40 /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
3 / 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 cocacola-lab/ChatIE, 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 clarify ChatIE's role as a zero-shot IE framework

    Why:

    CURRENT
    Official repository of paper "Zero-Shot Information Extraction via Chatting with ChatGPT". Please star, watch, and fork our repo for the active updates!
    COPY-PASTE FIX
    ChatIE is an open-source framework for **Zero-Shot Information Extraction (IE) using Large Language Models (LLMs)**, implementing the methodology from our paper 'Zero-Shot Information Extraction via Chatting with ChatGPT'. It enables researchers and practitioners to perform entity-relation triple extraction, named entity recognition, and event extraction without extensive data labeling.
  • mediumtopics#2
    Add more specific topics related to prompt engineering and zero-shot learning

    Why:

    CURRENT
    ai, chatgpt, chatgpt-app, event-extraciton, event-extraction, eventexecutor, information-extraction, knowledge-graph, llm, ner, nlp, openai, relation-extraction, tool, zero-shot
    COPY-PASTE FIX
    ai, chatgpt, chatgpt-app, event-extraction, information-extraction, knowledge-graph, llm, ner, nlp, openai, relation-extraction, tool, zero-shot, prompt-engineering, multi-turn-qa, zero-shot-learning, ie-framework
  • mediumreadme#3
    Add a clear statement about the project's license(s) to the README

    Why:

    COPY-PASTE FIX
    ## License
This project includes a `LICENSE` file detailing its terms of use. Please refer to this file for the specific licensing information, as it is not a standard SPDX template.

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 cocacola-lab/ChatIE
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI GPT-4 / GPT-3.5 Turbo
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI GPT-4 / GPT-3.5 Turbo · recommended 1×
  2. Anthropic Claude 3 (Opus/Sonnet/Haiku) · recommended 1×
  3. Google Gemini (Advanced/Pro) · recommended 1×
  4. Mistral Large / Mixtral 8x7B · recommended 1×
  5. Llama 3 (70B/8B) · recommended 1×
  • CATEGORY QUERY
    How to perform zero-shot information extraction from text using large language models?
    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. Mistral Large / Mixtral 8x7B
    5. Llama 3 (70B/8B)
    6. Cohere Command R+ / Command R

    AI recommended 6 alternatives but never named cocacola-lab/ChatIE. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools can help extract entities and events from text without extensive data labeling?
    you: not recommended
    AI recommended (in order):
    1. spaCy
    2. OpenNMT
    3. Stanford CoreNLP
    4. Flair
    5. Prodigy
    6. Haystack (by deepset.ai)
    7. Textacy

    AI recommended 7 alternatives but never named cocacola-lab/ChatIE. 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 cocacola-lab/ChatIE?
    pass
    AI named cocacola-lab/ChatIE explicitly

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

  • If a team adopts cocacola-lab/ChatIE in production, what risks or prerequisites should they evaluate first?
    pass
    AI named cocacola-lab/ChatIE 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 cocacola-lab/ChatIE solve, and who is the primary audience?
    pass
    AI named cocacola-lab/ChatIE explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
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