Awesome

ALTER: Augmentation for Large-Table-Based Reasoning

Official implementation of the paper "ALTER: Augmentation for Large-Table-Based Reasoning" . We warmly welcome discussions on table reasoning issues together!☺️

📝 Paper

ALTER: Augmentation for Large-Table-Based Reasoning

🚀 Installation

git clone https://anonymous.4open.science/r/tabular_data-295C
conda create -n alter python=3.10
conda activate alter
pip install -r requirements.txt

📍 Redis Store

You can run the experiments with LocalFile Store or Redis Store.

To run the experiments with Redis store, you can use the following command to start a Redis store in Docker:

docker run -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latest

🎯 Run

# modif config.yaml first
sh scripts/aug.sh
sh scripts/pipelines.sh

🧩 Data

We use the following datasets for the experiments:

• WikiTableQuestions

• TabFact

🌲Main File Tree

Our Code

• scripts: This directory contains scripts for the main experiments.

• config.yaml: This file contains the configs setting for the main experiments.

• data_loader: This directory consists of python scripts to create PyTorch Dataset for different datasets and preprocessing utilities used for experiments

• notebooks: This directory contains python notebooks that were used carry out the experiment in one run on benchmark datasets.

• prompt_manager: This directory mainly contains the prompt template to conduct experiments

• utils: This directory contains the code for all the util tools code for ALTER workflow, such as normalization or parsing.

• batch_pipe.py: This file is the main logic for running the ALTER workflow.

• run.py: start-up file for pipeline or augmentation process.

 File Tree:
|-- ./augmentation.py # script for augmentation in pre-stage
|-- ./batch_pipe.py 
|-- ./data_loader/__init__.py
|-- ./data_loader/datasets
|-- ./data_loader/table_augmentation.py
|-- ./data_loader/table_format.py
|-- ./data_loader/TableLoader.py
|-- ./executor/executor.py
|-- ./run.py
`-- ./utils.py
|-- notebook