ALTER: Augmentation for Large-Table-Based Reasoning
<|reference_start|>ALTER: Augmentation for Large-Table-Based Reasoning: 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.<|reference_end|>
@article{zhang2024alter:,
    title={ALTER: Augmentation for Large-Table-Based Reasoning},
    author={Han Zhang, Yuheng Ma, Hanfang Yang},
    journal={arXiv preprint arXiv:2407.03061},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03061},
    primaryClass={cs.CL}
}