ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold

ReSS is a framework that bridges symbolic and neural reasoning models for tabular data prediction, aiming for both high accuracy and understandable reasoning. It leverages decision trees to extract symbolic scaffolds that guide an LLM to generate natural-language reasoning, which is then used to fine-tune specialized tabular reasoning LLMs.