RRepoGEO

REPOGEO REPORT · LITE

castorini/rank_llm

Default branch main · commit a2a400ba · scanned 6/16/2026, 2:03:31 AM

GitHub: 604 stars · 91 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 castorini/rank_llm, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Strengthen the README's H1 to emphasize LLM-based listwise reranking

    Why:

    CURRENT
    # RankLLM
    COPY-PASTE FIX
    Change the H1 to: `# RankLLM: A Python Toolkit for Listwise Reranking with Large Language Models`
  • mediumreadme#2
    Explicitly list key features and differentiators in the README's 'Overview'

    Why:

    CURRENT
    Some of the code in this repository is borrowed from RankGPT, PyGaggle, and LiT5!
    COPY-PASTE FIX
    Expand the 'Overview' section to include a bulleted list of key features, such as:
*   Comprehensive suite of rerankers: pointwise (MonoT5), pairwise (DuoT5), and listwise models.
*   Strong focus on open-source LLMs compatible with vLLM, SGLang.
*   Support for proprietary listwise rerankers like RankGPT and RankGemini.
*   Efficiency improvements: reranking with first-token logits only.
*   Custom prompt template integration via YAML files.
*   New command-line interface (CLI) for ease of use.

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 castorini/rank_llm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ColBERT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ColBERT · recommended 1×
  2. MonoBERT · recommended 1×
  3. MonoT5 · recommended 1×
  4. BERT-base-uncased · recommended 1×
  5. RoBERTa-base · recommended 1×
  • CATEGORY QUERY
    How can I improve information retrieval system performance using large language models for reranking?
    you: not recommended
    AI recommended (in order):
    1. ColBERT
    2. MonoBERT
    3. MonoT5
    4. BERT-base-uncased
    5. RoBERTa-base
    6. Sentence-BERT
    7. BM25
    8. SPLADE

    AI recommended 8 alternatives but never named castorini/rank_llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python libraries are available for efficient listwise document reranking with open-source LLMs?
    you: not recommended
    AI recommended (in order):
    1. Haystack
    2. Hugging Face Transformers
    3. Llama.cpp
    4. CohereReranker
    5. LlamaIndex
    6. LangChain
    7. HuggingFacePipeline
    8. Sentence-Transformers
    9. RankGPT

    AI recommended 9 alternatives but never named castorini/rank_llm. 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 castorini/rank_llm?
    pass
    AI named castorini/rank_llm explicitly

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

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

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

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castorini/rank_llm — 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