RSAT: Structured Attribution Makes Small Language Models Faithful Table Reasoners

RSAT is a new method that trains small language models (SLMs) to produce faithful, step-by-step reasoning for table questions, grounded with cell-level citations. It significantly improves faithfulness (3.7x) and achieves near-perfect citation validity by integrating attribution into the reasoning process.