RRepoGEO

REPOGEO REPORT · LITE

TAG-Research/TAG-Bench

Default branch main · commit 76d5795d · scanned 6/3/2026, 9:32:50 PM

GitHub: 767 stars · 85 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 TAG-Research/TAG-Bench, 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
    Clarify 'TAG' definition and domain in README's H1

    Why:

    CURRENT
    # Text2SQL is Not Enough: Unifying AI and Databases with TAG
    COPY-PASTE FIX
    # TAG-Bench: A Benchmark for Table-Augmented Generation (TAG) in Natural Language Querying over Databases
  • mediumreadme#2
    Emphasize 'benchmark' and 'LLM evaluation' in the README's introductory paragraph

    Why:

    CURRENT
    Table-Augmented Generation (TAG) is a unified and general-purpose paradigm for answering natural language questions over databases. The TAG model represents a wide range of interactions between the LM and database that have been previously unexplored in methods such as Text2SQL and RAG. We provide the first benchmark to study the TAG problem and find that standard methods struggle to answer such queries, confirming the need for further research in this area.
    COPY-PASTE FIX
    TAG-Bench introduces the first comprehensive benchmark for Table-Augmented Generation (TAG), a unified paradigm for answering natural language questions over databases. This benchmark specifically evaluates LLMs on complex queries that require external knowledge or semantic reasoning beyond traditional Text2SQL or RAG. Our findings show that current models struggle with these advanced challenges, highlighting a critical need for further research and development in LLM evaluation for relational data.

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 TAG-Research/TAG-Bench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Neo4j
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Neo4j · recommended 1×
  2. Amazon Neptune · recommended 1×
  3. ArangoDB · recommended 1×
  4. Apache Jena · recommended 1×
  5. Stardog · recommended 1×
  • CATEGORY QUERY
    How to handle natural language database queries needing external knowledge beyond RAG?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. Amazon Neptune
    3. ArangoDB
    4. Apache Jena
    5. Stardog
    6. Virtuoso
    7. Prolog
    8. Datalog
    9. TypeDB
    10. DBPedia Spotlight
    11. GPT-4
    12. Llama 3

    AI recommended 12 alternatives but never named TAG-Research/TAG-Bench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are benchmarks for evaluating LLMs on complex natural language queries over relational data?
    you: not recommended
    AI recommended (in order):
    1. Spider
    2. WikiSQL
    3. CoSQL
    4. SParC
    5. BIRD (Big Bench for Robustness in NL2SQL)
    6. DuSQL
    7. TwiST

    AI recommended 7 alternatives but never named TAG-Research/TAG-Bench. 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 TAG-Research/TAG-Bench?
    pass
    AI named TAG-Research/TAG-Bench explicitly

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

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

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

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MARKDOWN (README)
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HTML
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TAG-Research/TAG-Bench — 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