@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://preview.aclanthology.org/transition-to-people-yaml/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."
}