REPOGEO REPORT · LITE

DEEP-PolyU/Awesome-LLM-based-Text2SQL

Default branch main · commit 1d7d8c52 · scanned 5/14/2026, 1:12:58 AM

GitHub: 1,313 stars · 121 forks

AI VISIBILITY SCORE

27 /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

1 / 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 DEEP-PolyU/Awesome-LLM-based-Text2SQL, 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 paragraph to explicitly state its nature as an 'Awesome List' and 'Survey'

Why:

CURRENT

This repository provides a comprehensive collection of research papers, benchmarks, and open-source projects on **large language model-based text-to-SQL (LLM-based Text-to-SQL)**. It includes all the contents from our survey paper 📖<em>"**Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL**"</em> and will be continuously updated to incorporate the up-to-date advances and notable contributions from the text-to-SQL community. Stay tuned!!

COPY-PASTE FIX

This **Awesome List** is a comprehensive, continuously updated collection of research papers, benchmarks, and open-source projects on **large language model-based text-to-SQL (LLM-based Text-to-SQL)**. It serves as the official companion to our survey paper 📖<em>"**Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL**"</em>, curating the latest advances and notable contributions from the text-to-SQL community.

mediumtopics#2

Add specific 'awesome-list' and 'survey-paper' topics

Why:

CURRENT

awesome, awesome-text-to-sql, awesome-text2sql, database, large-language-models, llms, natual-language-processing, natural-language-understanding, nl2sql, survey, text-to-sql, text2sql

COPY-PASTE FIX

awesome, awesome-list, awesome-text-to-sql, awesome-text2sql, database, large-language-models, llms, natual-language-processing, natural-language-understanding, nl2sql, survey, survey-paper, text-to-sql, text2sql

lowtopics#3
Correct typo in 'natural-language-processing' topic
Why:
CURRENT
```
natual-language-processing
```
COPY-PASTE FIX
```
natural-language-processing
```

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 DEEP-PolyU/Awesome-LLM-based-Text2SQL

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Papers With Code

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Papers With Code · recommended 1×
arXiv · recommended 1×
Google Scholar · recommended 1×
Hugging Face · recommended 1×
Towards Data Science · recommended 1×

CATEGORY QUERY
Where can I find a comprehensive survey of large language model text-to-SQL solutions?
you: not recommended
AI recommended (in order):
1. Papers With Code
2. arXiv
3. Google Scholar
4. Hugging Face
5. Towards Data Science
6. Kaggle
AI recommended 6 alternatives but never named DEEP-PolyU/Awesome-LLM-based-Text2SQL. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What open-source projects and benchmarks exist for converting natural language to SQL using LLMs?
you: not recommended
AI recommended (in order):
1. Spider
2. Picard
3. RAT-SQL
4. SQLova
5. NatSQL
6. WikiSQL
7. CoSQL
8. SParC
9. BIRD
AI recommended 9 alternatives but never named DEEP-PolyU/Awesome-LLM-based-Text2SQL. 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 DEEP-PolyU/Awesome-LLM-based-Text2SQL?
pass
AI did not name DEEP-PolyU/Awesome-LLM-based-Text2SQL — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts DEEP-PolyU/Awesome-LLM-based-Text2SQL in production, what risks or prerequisites should they evaluate first?
pass
AI named DEEP-PolyU/Awesome-LLM-based-Text2SQL 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 DEEP-PolyU/Awesome-LLM-based-Text2SQL solve, and who is the primary audience?
pass
AI did not name DEEP-PolyU/Awesome-LLM-based-Text2SQL — likely talking about a different project
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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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite