mahadi-nahid/TabSQLify
[NAACL 2024] TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition
This project helps data analysts and researchers get accurate answers from large tables using advanced language models, even when the tables are very big. You provide a question in plain language and a large table, and it gives you a precise answer by intelligently extracting only the relevant information. This is useful for anyone who needs to quickly get insights from extensive structured data without manually sifting through it.
Use this if you frequently ask questions about large tables and want a reliable way for an AI to find the answer efficiently.
Not ideal if your data is unstructured text rather than structured tables, or if you don't use large language models for data analysis.
Stars
17
Forks
1
Language
Python
License
—
Category
Last pushed
Jan 05, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/mahadi-nahid/TabSQLify"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
mlabonne/llm-datasets
Curated list of datasets and tools for post-training.
malteos/llm-datasets
A collection of datasets for language model pretraining including scripts for downloading,...
magpie-align/magpie
[ICLR 2025] Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing. Your...
jd-coderepos/llms4subjects
The official SemEval 2025 Task 5 - LLMs4Subjects - Shared Task Dataset repository
willxxy/ECG-Bench
A Unified Framework for Benchmarking Generative Electrocardiogram-Language Models (ELMs)