TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning
  in Tabular Data
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.
690b05add70e173c845290bd
Changjiang Jiang
claimed_verified
2025-11-05T16:24:21.778Z
652fc2605615e57807e3db19
https://cdn-avatars.huggingface.co/v1/production/uploads/652fc2605615e57807e3db19/kbRcpR0YFQnU3IlziqCHf.png
Changjiang Jiang
arnodjiang
user
arnodjiang
690b05add70e173c845290be
Fengchang Yu
690b05add70e173c845290bf
Haihua Chen
690b05add70e173c845290c0
Wei Lu
690b05add70e173c845290c1
Jin Zeng
TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning
  in Tabular Data
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.
A framework combining query decomposition, table sanitization, and program-of-thoughts reasoning improves large language models' performance on complex tabular numerical reasoning tasks.
query decomposer table sanitizer program-of-thoughts CalTab151 TAT-QA TableBench