@inproceedings{xiong-etal-2025-teleai,
title = "{T}ele{AI} at {S}em{E}val-2025 Task 8: Advancing Table Reasoning Framework with Large Language Models",
author = "Xiong, Sishi and
Li, Mengxiang and
Wang, Dakai and
Zhao, Yu and
Zhang, Jie and
Pan, Changzai and
He, Haowei and
Li, Xiangyu and
Chang, Wenhan and
He, Zhongjiang and
Song, Shuangyong and
Li, Yongxiang",
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-1.240/",
pages = "1828--1841",
ISBN = "979-8-89176-273-2",
abstract = "The paper presents our system developed for SemEval-2025 Task 8, which focuses on table question answering (TQA). The TQA tasks face challenges due to the characteristics of real-world tabular data, such as large size, incomplete column semantics, and entity ambiguity. To address these issues, we propose a large language model (LLM)-powered and programming-based framework, named Flow-of-Table-Reasoning. We introduce the table schema integrating verbalized structure and semantics for query decomposition and programming, enabling a holistic understanding of tables and the ability to process large-size tables. We design a multi-step schema linking plan to derive a focused table schema that retains only information relevant to the query, aiming to eliminate ambiguity and reduce hallucinations. Furthermore, we incorporate reasoning workflow into an iterative thinking architecture, allowing incremental cycles of thinking, reasoning and reflection. Our system achieves first place on both TQA and Lite TQA subtasks."
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<abstract>The paper presents our system developed for SemEval-2025 Task 8, which focuses on table question answering (TQA). The TQA tasks face challenges due to the characteristics of real-world tabular data, such as large size, incomplete column semantics, and entity ambiguity. To address these issues, we propose a large language model (LLM)-powered and programming-based framework, named Flow-of-Table-Reasoning. We introduce the table schema integrating verbalized structure and semantics for query decomposition and programming, enabling a holistic understanding of tables and the ability to process large-size tables. We design a multi-step schema linking plan to derive a focused table schema that retains only information relevant to the query, aiming to eliminate ambiguity and reduce hallucinations. Furthermore, we incorporate reasoning workflow into an iterative thinking architecture, allowing incremental cycles of thinking, reasoning and reflection. Our system achieves first place on both TQA and Lite TQA subtasks.</abstract>
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%0 Conference Proceedings
%T TeleAI at SemEval-2025 Task 8: Advancing Table Reasoning Framework with Large Language Models
%A Xiong, Sishi
%A Li, Mengxiang
%A Wang, Dakai
%A Zhao, Yu
%A Zhang, Jie
%A Pan, Changzai
%A He, Haowei
%A Li, Xiangyu
%A Chang, Wenhan
%A He, Zhongjiang
%A Song, Shuangyong
%A Li, Yongxiang
%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 xiong-etal-2025-teleai
%X The paper presents our system developed for SemEval-2025 Task 8, which focuses on table question answering (TQA). The TQA tasks face challenges due to the characteristics of real-world tabular data, such as large size, incomplete column semantics, and entity ambiguity. To address these issues, we propose a large language model (LLM)-powered and programming-based framework, named Flow-of-Table-Reasoning. We introduce the table schema integrating verbalized structure and semantics for query decomposition and programming, enabling a holistic understanding of tables and the ability to process large-size tables. We design a multi-step schema linking plan to derive a focused table schema that retains only information relevant to the query, aiming to eliminate ambiguity and reduce hallucinations. Furthermore, we incorporate reasoning workflow into an iterative thinking architecture, allowing incremental cycles of thinking, reasoning and reflection. Our system achieves first place on both TQA and Lite TQA subtasks.
%U https://aclanthology.org/2025.semeval-1.240/
%P 1828-1841
Markdown (Informal)
[TeleAI at SemEval-2025 Task 8: Advancing Table Reasoning Framework with Large Language Models](https://aclanthology.org/2025.semeval-1.240/) (Xiong et al., SemEval 2025)
ACL
- Sishi Xiong, Mengxiang Li, Dakai Wang, Yu Zhao, Jie Zhang, Changzai Pan, Haowei He, Xiangyu Li, Wenhan Chang, Zhongjiang He, Shuangyong Song, and Yongxiang Li. 2025. TeleAI at SemEval-2025 Task 8: Advancing Table Reasoning Framework with Large Language Models. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 1828–1841, Vienna, Austria. Association for Computational Linguistics.