@inproceedings{
chen2024tablerag,
title={Table{RAG}: Million-Token Table Understanding with Language Models},
author={Si-An Chen and Lesly Miculicich and Julian Martin Eisenschlos and Zifeng Wang and Zilong Wang and Yanfei Chen and Yasuhisa Fujii and Hsuan-Tien Lin and Chen-Yu Lee and Tomas Pfister},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=41lovPOCo5}
}
Recent advancements in language models (LMs) have notably enhanced their ability to reason with tabular data, primarily through program-aided mechanisms that manipulate and analyze tables.
However, these methods often require the entire table as input, leading to scalability challenges due to the positional bias or context length constraints.
In response to these challenges, we introduce TableRAG, a Retrieval-Augmented Generation (RAG) framework specifically designed for LM-based table understanding.
TableRAG leverages query expansion combined with schema and cell retrieval to pinpoint crucial information  before providing it to the LMs.
This enables more efficient data encoding and precise retrieval, significantly reducing prompt lengths and mitigating information loss.
We have developed two new million-token benchmarks from the Arcade and BIRD-SQL datasets to thoroughly evaluate TableRAG's effectiveness at scale.
Our results demonstrate that TableRAG's retrieval design achieves the highest retrieval quality, leading to the new state-of-the-art performance on large-scale table understanding.
TableRAG: Million-Token Table Understanding with Language Models
Si-An Chen Lesly Miculicich Julian Martin Eisenschlos Zifeng Wang Zilong Wang Yanfei Chen Yasuhisa Fujii Hsuan-Tien Lin Chen-Yu Lee Tomas Pfister