ALTER: Augmentation for Large-Table-Based Reasoning

• URL: http://arxiv.org/abs/2407.03061v1
• Date: Wed, 3 Jul 2024 12:34:45 GMT
• Title: ALTER: Augmentation for Large-Table-Based Reasoning
• Authors: Han Zhang, Yuheng Ma, Hanfang Yang,
• Abstract summary: ALTER(Augmentation for Large-Table-Based Reasoning) is a framework designed to harness the latent augmentation potential in both free-form natural language (NL) questions. By utilizing only a small subset of relevant data from the table, ALTER achieves outstanding performance on table-based reasoning benchmarks.
• Score: 5.164923314261229
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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