AI Tool for querying natural language on tabular data.
LLM-Powered Semi-Structured Table Question Answering
ICLR 2022 Paper, SOTA Table Pre-training Model, TAPEX: Table Pre-training via Learning a Neural SQL Executor
The Resources for "Natural Language to Logical Form" ; "自然语言转逻辑形式"研究资料收集。
resources for the IBM Airlines Table-Question-Answering Benchmark
Open-WikiTable :Dataset for Open Domain Question Answering with Complex Reasoning over Table
[NAACL 2024] TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition
(EMNLP 2025) Weaver: A modular agentic pipeline that dynamically combines SQL and LLMs for advanced table-based question answering
[ACML 2024] DataFrame QA: A Universal LLM Framework on DataFrame Question Answering Without Data Exposure
[EMNLP 2024] NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization
Explore how to build a Q&A system on Neo4j using Google's Palm2 model with MakerSuite in this repository.
TRACEBACK: multi-agent attribution framework, traces table-based answers back to their supporting cells, providing fine-grained attribution at the cell level
[ACML 2024] DataFrame QA: A Universal LLM Framework on DataFrame Question Answering Without Data Exposure
Streamlit components for FirstLanguage API
A small, locally-trained LLM answers constrained table questions and cites the exact cells it used: the model comprehends, a deterministic engine does the arithmetic. 59% to 96% answer accuracy on a locked test.
An offline, enterprise-grade Document Intelligence RAG system built to parse, search, query, and audit complex industrial engineering standard specifications. Features layout-grounded visual citations, coordinate-aware table intelligence, dynamic unit conversion, and strict compliance validation.
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