ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering

ACL ARR 2026 January Submission3862 Authors

Keywords: Table Question Answering, Large Language Models, Table Reasoning, Table Serialization

Abstract: Table serialization remains a critical bottleneck for Large Language Models (LLMs) in complex table question answering, hindered by challenges such as structural neglect, representation gaps, and reasoning opacity. Existing serialization methods fail to capture explicit hierarchies and lack schema flexibility, while current tree-based approaches suffer from limited semantic adaptability. To address these limitations, we propose **ASTRA** (**A**daptive **S**emantic **T**ree **R**easoning **A**rchitecture) including two main modules, **AdaSTR** and **DuTR**. First, we introduce **AdaSTR**, which leverages the global semantic awareness of LLMs to reconstruct tables into Logical Semantic Trees. This serialization explicitly models hierarchical dependencies and employs an adaptive mechanism to optimize construction strategies based on table scale. Second, building on this structure, we present **DuTR**, a dual-mode reasoning framework that integrates tree-search-based textual navigation for linguistic alignment and symbolic code execution for precise verification. Experiments on complex table benchmarks demonstrate that our method achieves state-of-the-art (SOTA) performance.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: table QA

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 3862