AixelAsk: A Stepwise-Guided Retrieval and Reasoning Framework for Large Table QA

In the big data era, Table Question Answering (Table QA) has emerged as a crucial tool for extracting insights from structured data, especially in large table scenarios. There are two main categories of methods for Table QA: Executable Code-driven methods and Language Model based (LM-based) methods. Code-driven methods, e.g. Text-to-SQL based solutions, often struggle with incomplete or mismatching schema information. LM-based methods, include Pre-trained Language Models (PLMs) and Large Language Models (LLMs), also face challenges as PLMs have limited generalization, while LLMs suffer from performance degradation and increased token cost when applied to large tables. To address these challenges, we propose AixelAsk, a novel LLM-based framework designed for Large Table QA. Specifically, AixelAsk incorporates a three-module architecture consisting of Decomposition module, Retrieval module and Reasoning module. The Decomposition module constructs a directed acyclic graph (DAG)-based solution plan by decomposing the question into execution nodes with explicit dependencies, making a clear reasoning path to guide the LLM through a logical process. Inspired by the Retrieval-Augmented Generation, the Retrieval Module extracts key rows and columns from the large table, reducing input token size and focusing on critical information. The Reasoning Module performs step-by-step inferences over the retrieved sub-tables, guided by each execution node in the solution plan, to generate final answer. By tackling the challenges of LLM performance degradation with large inputs and complex questions, AixelAsk achieves superior performance in Large Table QA. Extensive experiments on various baselines across three datasets demonstrate the effectiveness and efficiency of our proposed AixelAsk framework. AixelAsk outperforms the state-of-the-art baseline by 4% - 8% in the exact match score, and at the same time reduces token usage by 86.4%, achieving both high accuracy and cost efficiency in the Large Table QA task.