T ele AI at S em E val-2025 Task 8: Advancing Table Reasoning Framework with Large Language Models (2025)

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.

## introduction table question answering is a spin-off question answering (qa) task, aiming at responding to natural language questions based on table data that feature heterogeneous and complex two-dimensional structure @xcite . compared to tasks involving unstructured or plain text, tqa faces greater challenges and emphasizes the model's reasoning abilities. the primary challenges encompass large size, incomplete semantics, and entity ambiguity. to advance research on table understanding by applying language models, semeval-2025 introduces task 8: question answering on tabular data @xcite . in this paper, we present tablereasoner, a systematic llm-powered and programming-based framework for tqa. we introduce the table schema as the table representation, instead of entire or truncated table text in conventional format (such as csv or markdown), enabling our framework capable of processing large tables. tablereasoner employs a programming module to solve questions using the table schema to understand the table content from a holistic perspective. to ensure precise scheduling for query decomposition and programming, we design a "parsing-linking-refinement" action flow. it refines the global table schema into a focused one only associated with the original query, to some extent alleviating model hallucinations. in addition, inspired by the react @xcite paradigm, we incorporate the reasoning workflow into a "thought-action-observation" architecture, facilitating iterative cycles of thinking, reasoning and reflection within our system. tablereasoner is flexible to any advanced language model. it delivers excellent results even without fine-tuning, while further performance enhancements can be achieved through fine-tuning and majority voting. our system wins first place on both subtasks, validating the superiority and scalability of our framework. ## system overview our system is implemented using tablereasoner, an llm-powered and programming-based framework, as shown in figure 1 . it's designed to integrate a reasoning workflow into an iterative thinking process. ## experimental setup model foot_1 . we conduct extensive experiments on popular llms, including the open-sourced qwen2.5 series @xcite , llama3 series @xcite , telechat2-35b @xcite , mistral-large foot_2 and close-sourced gpt-4o (openai, 2023). gpt-4o is invoked via the official api interface, and other models are deployed and invoked locally. 5 results and analysis