  While extensive research has explored the use of large language models (LLMs)
for table-based reasoning, most approaches struggle with scalability when
applied to large tables. To maintain the superior comprehension abilities of
LLMs in these scenarios, we introduce ALTER(Augmentation for Large-Table-Based
Reasoning)-a framework designed to harness the latent augmentation potential in
both free-form natural language (NL) questions, via the query augmentor, and
semi-structured tabular data, through the table augmentor. By utilizing only a
small subset of relevant data from the table and supplementing it with
pre-augmented schema, semantic, and literal information, ALTER achieves
outstanding performance on table-based reasoning benchmarks. We also provide a
detailed analysis of large-table scenarios, comparing different methods and
various partitioning principles. In these scenarios, our method outperforms all
other approaches and exhibits robustness and efficiency against perturbations.
