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RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals

Paper · 2025 · Xuanliang Zhang, Dingzirui Wang, Keyan Xu et al.

Summary

Reasoning LLMs that produce Long CoT have become strong at table question answering. However, building them requires costly training on curated data, and their long outputs tend to lose track of the table and invent cell contents. The authors (Harbin Institute of Technology) propose Row-of-Thought (RoT), a method based purely on prompting. The model reads a Markdown-formatted table one row at a time and updates an intermediate result after each row. After every full pass, it decides whether to keep reasoning (for multi-hop questions), re-check its earlier steps and traverse the table again, or stop with an answer. Non-reasoning models receive a one-shot prompt, and R1-distilled models a zero-shot one. Evaluation used WikiTableQuestions, HiTab and TableBench. Ordinary instruction-tuned Llama and Qwen models with RoT beat same-sized DeepSeek-R1 distills running Long CoT by 4.3% on average, while spending fewer reasoning tokens. The work suggests that forcing systematic attention to every row can replace expensive reasoning training and reduce table hallucinations.

Key points

  • Models compared: Llama3.1-8B, Llama3.3-70B, Qwen2.5-7B and Qwen2.5-32B with RoT were compared against R1-Llama-8B/70B and R1-Qwen-7B/32B with Long CoT.
  • Metrics: WikiTQ and HiTab were scored by exact-match accuracy, and TableBench by Rouge-L.
  • Gains over Short CoT:
    • Llama3.3-70B rises from 72.7 to 78.7 on WikiTQ.
    • Llama3.1-8B rises from 46.5 to 56.6 on HiTab.
  • Effect on reasoning models: RoT also helps the RLLMs themselves, adding 2.4% on average over their Long CoT. It reaches SOTA among comparable models on WikiTQ and TableBench.
  • Limitations:
    • Testing covers English datasets only, which is relevant for Ukrainian applications.
    • Multi-turn table QA was not covered.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Harbin Institute of TechnologyHarbin · China158 works · 28,841 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,426 works

Cite

Cite as Xuanliang Zhang, Dingzirui Wang, Keyan Xu, Qingfu Zhu and Wanxiang Che. 2025. RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.emnlp-main.29

@inproceedings{zhang2025rot,
  title = {{RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals}},
  author = {Xuanliang Zhang, Dingzirui Wang, Keyan Xu, Qingfu Zhu and Wanxiang Che},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.29},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.29}
}

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