TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning
  in Tabular Data
Authors: name: Changjiang Jiang, name: Fengchang Yu, name: Haihua Chen, name: Wei Lu, name: Jin Zeng

Complex reasoning over tabular data is crucial in real-world data analysis,
yet large language models (LLMs) often underperform due to complex queries,
noisy data, and limited numerical capabilities. To address these issues, we
propose \method, a framework consisting of: (1) a query decomposer that breaks
down complex questions, (2) a table sanitizer that cleans and filters noisy
tables, and (3) a program-of-thoughts (PoT)-based reasoner that generates
executable code to derive the final answer from the sanitized table. To ensure
unbiased evaluation and mitigate data leakage, we introduce a new dataset,
CalTab151, specifically designed for complex numerical reasoning over tables.
Experimental results demonstrate that \method consistently outperforms existing
methods, achieving state-of-the-art (SOTA) performance with 8.79%, 6.08%, and
19.87% accuracy improvement on TAT-QA, TableBench, and \method, respectively.
Moreover, our framework integrates seamlessly with mainstream LLMs, providing a
robust solution for complex tabular numerical reasoning. These findings
highlight the effectiveness of our framework in enhancing LLM performance for
complex tabular numerical reasoning. Data and code are available upon request.