HITSZ - HLT at S em E val-2025 Task 8: Multi-turn Interactive Code Generation for Question Answering on Tabular Data (2025)

Abstract: This paper introduces the system developed by the HITSZ-HLT team for SemEval-2025 Task 8: DataBench, Question-Answering over Tabular Data.The primary objective of Table Question Answering (TableQA) is to provide accurate answers to user queries by interpreting and understanding tabular data. To address this, we propose the Multi-turn Interactive Code GeneratiOn(MICO) framework. Specifically, MICO employs code generation as proxy task for TableQA and integrates feedback from the execution of the generated code via multi-turn dialogue process, thereby guiding the model towards self-correction.Experimental results demonstrate the effectiveness of our framework, achieving notable performance with a rank of 4/38 on the DataBench and 5/38 on the DataBench lite.

## introduction table question answering (tableqa) @xcite @xcite has gained significant attention due to the extensive use of tabular data in various domains @xcite . the primary objective of tableqa is to accurately interpret and process tabular data, enabling autonomous generation of answers to user queries. as shown in fig. 1 , the goal of the model to leverage the information provided within the table to identify the author of the shortest post. by empowering machines to reason over structured data in tables, tableqa systems seek to offer a more effective and efficient approach to interacting with large datasets @xcite . while this task holds great potential, it is also accompanied by several challenges @xcite . the inherent complexity of tabular data, which includes large datasets @xcite , unordered structures, and high-precision numerical table: post info values, presents significant obstacles in generating accurate and efficient responses to queries. in this paper, we propose multi-turn interactive code generation (mico). specifically, we leverage code generation tasks as a surrogate for tableqa to reduce the model's complexity in processing long-context inputs and performing precise numerical computations. initially, we integrate the table's metadata along with a few sample instances into the prompt, thereby directing the model to generate code. the generated code is then executed within a sandboxed environment foot_0 to acquire feedback. in subsequent rounds of interaction, if the model determines the code has been successfully executed, it will regenerate the structured output and deliver the final result. conversely, if the execution is deemed unsuccessful, the model will engage in self-correction and restart the process. additionally, we conducted a thorough evaluation of our approach using the databench and databench lite datasets, which provided strong evidence of its effectiveness. this extensive validation process resulted in impressive performance, securing a ranking of 4th out of 38 participants on the databench leaderboard and 5th out of 38 on the databench lite leaderboard, as reported on the official rankings. moreover, additional experiments further confirm the effectiveness of each component. ## related work existing tableqa research primarily focuses on two main directions: semantic parsing and query generation. research in the semantic parsing direction involves joint training of natural language questions and tabular text data, followed by finetuning for specific tabular tasks, enabling the model to understand the semantic information in the table and provide accurate answers @xcite @xcite . another approach is query generation, where natural language questions are transformed into formal query languages to retrieve relevant data from the table for answering @xcite @xcite . these approaches assumes that the table has a known, well-structured format suitable for query translation, but its effectiveness may be challenged when dealing with complex or unstructured tables. however, current researches still lack exploration of multi-turn dialogue. our work is the first to explore the use of multi-turn dialogue for self-correction in the code generation process for tableqa. ## method in this section, we introduce the system used, which addresses the question answering on tabular data task through an multi-turn interactive code generation approach, as shown in fig. 2 . ## conclusion in this paper, we introduced the mico framework for tableqa, aiming to enhance the accuracy and efficiency of answering queries over tabular data. by utilizing code generation as a proxy task for tableqa and incorporating multi-turn dialogue for feedback and self-correction, mico effectively addresses the challenges of processing large, complex datasets and performing precise numerical computations. our experimental results, conducted on the databench and databench lite datasets, demonstrated the effectiveness of the mico framework, with our system achieving a commendable rank of 4th out of 38 participants on the databench leaderboard and 5th out of 38 on the databench lite leaderboard.