RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals

TL;DR

RoT introduces an iterative row-wise traversal method for table reasoning that enhances accuracy, reduces hallucinations, and is training-free, outperforming existing large language model approaches on benchmark datasets.

Contribution

The paper proposes RoT, a novel, training-free, row-wise traversal method that improves table reasoning by enabling iterative reasoning and reflection, reducing hallucinations and increasing efficiency.

Findings

RoT outperforms RLLMs by 4.3% on average.

Achieves state-of-the-art results on WikiTableQuestions and TableBench.

Requires fewer reasoning tokens than Long CoT.

Abstract

The table reasoning task, crucial for efficient data acquisition, aims to answer questions based on the given table. Recently, reasoning large language models (RLLMs) with Long Chain-of-Thought (Long CoT) significantly enhance reasoning capabilities, leading to brilliant performance on table reasoning. However, Long CoT suffers from high cost for training and exhibits low reliability due to table content hallucinations. Therefore, we propose Row-of-Thought (RoT), which performs iteratively row-wise table traversal, allowing for reasoning extension and reflection-based refinement at each traversal. Scaling reasoning length by row-wise traversal and leveraging reflection capabilities of LLMs, RoT is training-free. The sequential traversal encourages greater attention to the table, thus reducing hallucinations. Experiments show that RoT, using non-reasoning models, outperforms RLLMs by an average of 4.3%, and achieves state-of-the-art results on WikiTableQuestions and TableBench with comparable models, proving its effectiveness. Also, RoT outperforms Long CoT with fewer reasoning tokens, indicating higher efficiency.