Introduction
Tabular data is one of the fundamental and pivotal semi-structured data types widely used in relational databases, spreadsheets, analysis reports, etc . Table-based reasoning tasks such as table-based fact verification (FV) Aly et al. ( 2021 ); Chen et al. ( 2020a ); Ou and Liu ( 2022 ) and table-based question answering (TQA) Chen et al. ( 2020b ); Pasupat and Liang ( 2015 ); Iyyer et al. ( 2017 ) require sophisticated reasoning over textual, numerical, and logical forms. Besides, the reasoning tasks with large data capacity pose more complexity and challenges to machine intelligence.

Recently, large language models (LLMs) have demonstrated remarkable proficiency in reasoning and inference. The advent of LLMs has spurred a surge in research focusing on their application to tabular data, heralding what can be termed the LLM era Zhang et al. ( 2024 ); Lu et al. ( 2024 ) . Despite techniques following the pre-LLM era , such as fine-tuning methods, the latest LLM-based approaches have achieved results that are on par with or surpass those obtained through rule-based or pre-trained language model approaches Liu et al. ( 2022 ); Gu et al. ( 2022 ); Jin et al. ( 2022 ) , leveraging the contextual understanding capabilities of LLMs.

Mainstream techniques addressing tabular tasks in the LLM era focus on designing prompts or pipelines that combine multiple instructions with serialized natural language descriptions converted from tables, without requiring additional training. The sequential text data is parsed by the LLMs and transformed into executable code ( e.g., SQL and Python) using symbolic code generation abilities Zan et al. ( 2023 ); Cheng et al. ( 2023 ) or direct output for final inference utilizing literal reasoning abilities Jiang et al. ( 2023 ); Gong et al. ( 2020 ) .

However, most table-based methods encounter three challenges when analyzing complex large tables. Firstly , in the process of converting table cells into natural language descriptions, the entire data is often expected to be included to provide enough comprehensive information Cheng et al. ( 2023 ) . This approach can sometimes face data leakage issues involving privacy concerns and may fail due to context length limitations. Additionally, the full table content can be long and noisy, leading to unnecessary computational resource consumption. Secondly , table reasoning tasks often require numerical reasoning, data preparation, or key cell identification. LLMs alone may lack the robustness to address these tasks directly and can sometimes introduce inaccuracies or hallucinations in their outputs.
As tables grow in size, reasoning about minor or nuanced details becomes even more difficult (Liu et al., 2024 ) , and LLMs require careful design to enhance their expandability and robustness in such scenarios. Thirdly , relevant parts needed to derive the answer may be scattered in different places for a complex large-table reasoning task. Therefore, intricate queries cannot be answered in a single glance or with a single execution step using programming languages. Although a couple of methods have optimized for specific issues mentioned above, no approach simultaneously considers all these problems while extending table-based reasoning tasks to large-scale tables.

In consideration of the issues mentioned above, how can we mitigate performance degradation as the size of the table increases? We note that tables are inherently structured; in real-world databases, for instance, tables are well-categorized, and each column feature adheres to certain criteria, including data format, text representation, feature semantics, etc . Based on these practical observations, in this paper, we propose a framework named ALTER to facilitate the understanding of tables and to scale effectively to large-scale tables. Without utilizing the entire table data as contextual information throughout the process, we first generate adaptations about the NL questions with the query augmentor in Section 4.2 and interpretations about the table’s inherent structure and content with the table augmentor in Section 4.3.1 . Subsequently, the data is further distilled to filter irrelevant column features. In conjunction with augmented information, the well-organized data is integrated with SQL executors and ultimately transformed into a more accessible format for joint reasoning, adhering to the proposed augment-filter-execution procedure.

In summary, our main contributions include: (i) We explore new augmentation methods for queries and tables that are beneficial for table-based reasoning tasks.
(ii) We propose a general framework and a novel augment-filter-execution procedure capable of scaling to large tables. (iii) We conduct extensive experiments on two table-based reasoning benchmarks and demonstrate the best performance over large-table scenarios.