@inproceedings{yao-etal-2025-111dut,
title = "111{DUT} at {S}em{E}val-2025 Task 8:Hierarchical Chain-of-Thought Reasoning and Multi-Model Deliberation for Robust {T}able{QA}",
author = "Yao, Jiaqi and
Yu, Erchen and
Tian, Yicen and
Kang, Yiyang and
Zhang, Jiayi and
Lin, Hongfei and
Zong, Linlin and
Xu, Bo",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.32/",
pages = "216--221",
ISBN = "979-8-89176-273-2",
abstract = "The proliferation of structured tabular data in domains like healthcare and finance has intensified the demand for precise table question answering, particularly for complex numerical reasoning and cross-domain generalization. Existing approaches struggle with implicit semantics and multi-step arithmetic operations. This paper presents our solution for SemEval-2025 task,including three synergistic components: (1) a Schema Profiler that extracts structural metadata via LLM-driven analysis and statistical validation, (2) a Hierarchical Chain-of-Thought module that decomposes questions into four stages(semantic anchoring, schema mapping, query synthesis, and self-correction)to ensure SQL validity, and (3) a Confidence-Accuracy Voting mechanism that resolves discrepancies across LLMs through weighted ensemble decisions. Our framework achieves scores of 81.23 on Databench and 81.99 on Databench{\_}lite, ranking 6th and 5th respectively, demonstrating the effectiveness of structured metadata guidance and cross-model deliberation in complex TableQA scenarios."
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<abstract>The proliferation of structured tabular data in domains like healthcare and finance has intensified the demand for precise table question answering, particularly for complex numerical reasoning and cross-domain generalization. Existing approaches struggle with implicit semantics and multi-step arithmetic operations. This paper presents our solution for SemEval-2025 task,including three synergistic components: (1) a Schema Profiler that extracts structural metadata via LLM-driven analysis and statistical validation, (2) a Hierarchical Chain-of-Thought module that decomposes questions into four stages(semantic anchoring, schema mapping, query synthesis, and self-correction)to ensure SQL validity, and (3) a Confidence-Accuracy Voting mechanism that resolves discrepancies across LLMs through weighted ensemble decisions. Our framework achieves scores of 81.23 on Databench and 81.99 on Databench_lite, ranking 6th and 5th respectively, demonstrating the effectiveness of structured metadata guidance and cross-model deliberation in complex TableQA scenarios.</abstract>
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%0 Conference Proceedings
%T 111DUT at SemEval-2025 Task 8:Hierarchical Chain-of-Thought Reasoning and Multi-Model Deliberation for Robust TableQA
%A Yao, Jiaqi
%A Yu, Erchen
%A Tian, Yicen
%A Kang, Yiyang
%A Zhang, Jiayi
%A Lin, Hongfei
%A Zong, Linlin
%A Xu, Bo
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F yao-etal-2025-111dut
%X The proliferation of structured tabular data in domains like healthcare and finance has intensified the demand for precise table question answering, particularly for complex numerical reasoning and cross-domain generalization. Existing approaches struggle with implicit semantics and multi-step arithmetic operations. This paper presents our solution for SemEval-2025 task,including three synergistic components: (1) a Schema Profiler that extracts structural metadata via LLM-driven analysis and statistical validation, (2) a Hierarchical Chain-of-Thought module that decomposes questions into four stages(semantic anchoring, schema mapping, query synthesis, and self-correction)to ensure SQL validity, and (3) a Confidence-Accuracy Voting mechanism that resolves discrepancies across LLMs through weighted ensemble decisions. Our framework achieves scores of 81.23 on Databench and 81.99 on Databench_lite, ranking 6th and 5th respectively, demonstrating the effectiveness of structured metadata guidance and cross-model deliberation in complex TableQA scenarios.
%U https://aclanthology.org/2025.semeval-1.32/
%P 216-221
Markdown (Informal)
[111DUT at SemEval-2025 Task 8:Hierarchical Chain-of-Thought Reasoning and Multi-Model Deliberation for Robust TableQA](https://aclanthology.org/2025.semeval-1.32/) (Yao et al., SemEval 2025)
ACL
- Jiaqi Yao, Erchen Yu, Yicen Tian, Yiyang Kang, Jiayi Zhang, Hongfei Lin, Linlin Zong, and Bo Xu. 2025. 111DUT at SemEval-2025 Task 8:Hierarchical Chain-of-Thought Reasoning and Multi-Model Deliberation for Robust TableQA. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 216–221, Vienna, Austria. Association for Computational Linguistics.