RRepoGEO

REPOGEO REPORT · LITE

VITA-MLLM/Woodpecker

Default branch main · commit 7a31dfec · scanned 5/30/2026, 6:08:16 PM

GitHub: 649 stars · 29 forks

AI VISIBILITY SCORE
35 /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
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 VITA-MLLM/Woodpecker, 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 opening to immediately state Woodpecker's identity as a correction framework

    Why:

    CURRENT
    > Hallucination is a big shadow hanging over the rapidly evolving Multimodal Large Language Models (MLLMs), referring to the phenomenon that the generated text is inconsistent with the image content. In order to mitigate hallucinations, existing studies mainly resort to an instruction-tuning manner that requires retraining the models with specific data. In this paper, we pave a different way, introducing a training-free method named Woodpecker.
    COPY-PASTE FIX
    > Woodpecker is a novel, training-free framework designed for hallucination correction in Multimodal Large Language Models (MLLMs). Unlike existing methods that require retraining, Woodpecker operates as a post-remedy solution, identifying and correcting inconsistencies between generated text and image content. This framework offers an interpretable, five-stage process for mitigating MLLM hallucinations.
  • highlicense#2
    Add a standard open-source license file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file (e.g., `LICENSE.md`) in the repository root containing the text of a widely recognized open-source license such as the MIT License.
  • mediumhomepage#3
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    In the repository settings, set the 'Homepage' URL to `https://arxiv.org/abs/2311.17028` (or the demo link if preferred).

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 VITA-MLLM/Woodpecker
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. GPT-4V · recommended 1×
  3. Gemini · recommended 1×
  4. Claude 3 Opus · recommended 1×
  5. BLIP-2 · recommended 1×
  • CATEGORY QUERY
    How can I correct hallucinations in multimodal large language models without retraining?
    you: not recommended
    AI recommended (in order):
    1. GPT-4V
    2. Gemini
    3. Claude 3 Opus
    4. BLIP-2
    5. ViT-GPT2
    6. Tesseract (tesseract-ocr/tesseract)
    7. Google Cloud Vision API
    8. LangChain (langchain-ai/langchain)
    9. LlamaIndex (run-llama/llama_index)
    10. Pinecone
    11. Weaviate (weaviate/weaviate)
    12. ChromaDB (chroma-core/chroma)
    13. OpenSearch (opensearch-project/OpenSearch)
    14. Elasticsearch (elastic/elasticsearch)
    15. Wikidata
    16. Google Knowledge Graph API
    17. Google Search API
    18. Bing Web Search API
    19. Wolfram Alpha API
    20. OpenAI Moderation API
    21. Scale AI
    22. Appen
    23. Argilla (argilla-io/argilla)

    AI recommended 23 alternatives but never named VITA-MLLM/Woodpecker. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective post-processing methods to improve factual consistency in MLLM outputs?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. Virtuoso
    3. Wikidata Query Service
    4. Google Fact Check Tools API
    5. Snopes
    6. PolitiFact
    7. Elasticsearch
    8. Pinecone
    9. FAISS
    10. Wikipedia
    11. PubMed
    12. GPT-4
    13. Claude 3
    14. Gemini Advanced
    15. spaCy
    16. NLTK
    17. Label Studio
    18. Prodigy

    AI recommended 18 alternatives but never named VITA-MLLM/Woodpecker. 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 VITA-MLLM/Woodpecker?
    pass
    AI named VITA-MLLM/Woodpecker explicitly

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

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

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

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VITA-MLLM/Woodpecker — 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