TIGER-AI-Lab/TableCoT
The code and data for paper "Large Language Models are few(1)-shot Table Reasoners" [EACL2023]
This helps researchers evaluate how well large language models can answer complex questions using information found in tables. You provide the model with tabular data and a set of questions, and it outputs the model's answers, along with a score indicating accuracy. It's designed for AI researchers and practitioners working on natural language processing and question answering systems.
No commits in the last 6 months.
Use this if you are an AI researcher or developer wanting to benchmark the performance of large language models on table-based question answering tasks.
Not ideal if you're looking for a user-friendly tool to extract insights from tables without needing to evaluate model performance.
Stars
48
Forks
3
Language
Python
License
—
Category
Last pushed
Apr 30, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/TIGER-AI-Lab/TableCoT"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
ExtensityAI/symbolicai
A neurosymbolic perspective on LLMs
TIGER-AI-Lab/MMLU-Pro
The code and data for "MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding...
deep-symbolic-mathematics/LLM-SR
[ICLR 2025 Oral] This is the official repo for the paper "LLM-SR" on Scientific Equation...
microsoft/interwhen
A framework for verifiable reasoning with language models.
zhudotexe/fanoutqa
Companion code for FanOutQA: Multi-Hop, Multi-Document Question Answering for Large Language...