Year: 2025
Type: conference-paper
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 Rowof-Thought (ROT), which performs iteratively row-wise table traversal, allowing for reasoning extension and reflection-based refinement at each traversal.Scaling reasoning length by rowwise 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.
Institution: Harbin Institute of Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G2565984771, G3596954764, G4035621038
OpenAlex ID: W4416037136