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.

Tabular data in scientific papers provides valuable structured information for knowledge discovery and validation. Although the language models such as BERT and ChatGPT have significantly advanced the research on general domain tables, challenges remain in scientific tables. Specifically, such models have limitations in understanding scientific entities, as well as lacks numerical representation and computation capabilities. Previous studies have focused on scientific tables, but they are limited to individual modules or tasks and lack a comprehensive framework. To address these issues, we introduce a reading comprehension framework for scientific tables, named NRTR, which uses a multi-task learning approach that shares a common encoder, achieves reasoning across various tasks, including question answering, cloze testing, and fact verification. It has the following characteristics: (1) utilizing entity linking and named entity recognition to extract key information from papers, which enhances the models' understanding of scientific entities; (2) injecting numerical representation capabilities into language models and promoting the model's understanding of the relative magnitude of numbers to better reason about maximum and difference values. Notably, the existing scientific corpus lacks tabular contexts or does not integrate computational reasoning, which hinders the evaluation of reasoning models in scientific tables. To this end, we release SciTab, a multi-task dataset that merges high-quality scientific tables with contextual information to provide a benchmark for future research. Our experimental results show that NRTR outperforms existing models on SciTab.