arXiv:2604.08999v2 Announce Type: replace-cross 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 (Adaptive Semantic Tree Reasoning Architecture) including two main modules, AdaSTR and DuTR. First, we i
The proliferation of Large Language Models (LLMs) has amplified the limitations of current table serialization methods, necessitating more sophisticated approaches for complex data interpretation and reasoning.
Improved table question answering capabilities would significantly enhance the practical utility of LLMs in enterprise environments, data analysis, and scientific research.
This research suggests a potential advancement in LLM's ability to accurately and adaptively process complex tabular data, moving beyond current limitations of structural neglect and reasoning opacity.
- · AI developers
- · Data scientists
- · Enterprises reliant on data analytics
- · LLM applications
- · Legacy data processing methods
- · LLM competitors with weaker table understanding
- · Manual data extraction services
LLMs can interpret and reason over complex tables with greater accuracy and flexibility.
This improvement could lead to more robust AI agents for business intelligence, report generation, and data-driven decision making.
The enhanced ability of AI to derive insights from structured data could accelerate automation across various white-collar workflows, further impacting sectors like finance and consulting.
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