RRepoGEO

REPOGEO REPORT · LITE

bird-bench/BIRD-Interact

Default branch main · commit 451fe2c3 · scanned 5/26/2026, 3:17:58 PM

GitHub: 1,003 stars · 19 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 bird-bench/BIRD-Interact, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    text-to-sql, nlp, natural-language-processing, llm, large-language-models, evaluation-benchmark, interactive-ai, conversational-ai, database-interface, iclr-2026
  • highreadme#2
    Reposition the README's opening to clearly state its purpose as an evaluation benchmark

    Why:

    COPY-PASTE FIX
    BIRD-INTERACT is a novel evaluation benchmark designed specifically for interactive, multi-turn Text-to-SQL models, addressing the limitations of static benchmarks by incorporating dynamic user interactions and dialogue history.
  • mediumreadme#3
    Emphasize the unique interactive and multi-turn differentiator in the README

    Why:

    COPY-PASTE FIX
    Unlike traditional static Text-to-SQL benchmarks, BIRD-INTERACT introduces an interactive, multi-turn evaluation paradigm. This allows for a more realistic assessment of models by incorporating user feedback and dialogue history, which is crucial for real-world conversational database interfaces.

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 bird-bench/BIRD-Interact
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
SQLFlow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. SQLFlow · recommended 1×
  2. Jupyter Notebooks · recommended 1×
  3. Google Colab · recommended 1×
  4. SQLAlchemy · recommended 1×
  5. Pandas · recommended 1×
  • CATEGORY QUERY
    What are the best tools for evaluating text-to-SQL model performance with interactive queries?
    you: not recommended
    AI recommended (in order):
    1. SQLFlow
    2. Jupyter Notebooks
    3. Google Colab
    4. SQLAlchemy
    5. Pandas
    6. psycopg2
    7. mysql-connector-python
    8. DataGrip
    9. DBeaver
    10. Flask
    11. Django
    12. Django ORM
    13. React
    14. Vue
    15. Jinja templates

    AI recommended 15 alternatives but never named bird-bench/BIRD-Interact. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I benchmark text-to-SQL systems considering dynamic conversational context?
    you: not recommended
    AI recommended (in order):
    1. Spider-DK
    2. Spider-Syn
    3. SParC
    4. CoSQL
    5. CHASE
    6. NL2SQL-Chat
    7. GPT-4
    8. Claude 3 Opus
    9. LlamaIndex
    10. LangChain

    AI recommended 10 alternatives but never named bird-bench/BIRD-Interact. 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 bird-bench/BIRD-Interact?
    pass
    AI named bird-bench/BIRD-Interact explicitly

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

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

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

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bird-bench/BIRD-Interact — 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