Generalizing Numerical Reasoning in Table Data through Operation Sketches and Self-Supervised Learning
arXiv:2604.21495v2 Announce Type: replace-cross Abstract: Numerical reasoning over expert-domain tables often exhibits high in-domain accuracy but limited robustness to domain shift. Models trained with supervised fine-tuning (SFT) on specific datasets tend to rely on header-operation shortcuts rather than structural reasoning. We introduce TaNOS, a continual pre-training framework comprising three components: (i) header anonymization to reduce lexical memorization, (ii) operation sketches that provide minimal structural cues, and (iii) self-supervised pretraining that constructs correctness-g
The continuous drive for more robust and generalizable AI models necessitates innovation in training methodologies that overcome limitations of supervised fine-tuning.
Improving numerical reasoning in AI for expert-domain tables is critical for automating complex analytical tasks across various industries and reducing errors.
The TaNOS framework introduces a potentially more robust and generalizable approach to training AI models for numerical reasoning, moving beyond simple reliance on data shortcuts.
- · AI researchers in numerical reasoning
- · Industries relying on complex table data analysis
- · Developers of self-supervised learning methods
- · AI models overly reliant on naive supervised fine-tuning
More accurate and reliable AI systems for financial analysis, scientific research, and operational planning.
Reduced need for extensive domain-specific labeled datasets for training numerical reasoning models.
Acceleration of AI applications in highly specialized and data-intensive fields where current generalization is a bottleneck.
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Read at arXiv cs.CL