Synthetic Contrastive Reasoning for Multi-Table Q&A

This paper introduces a synthetic contrastive reasoning-trace dataset for multi-table question answering (MMQA), addressing the lack of reasoning supervision in existing resources. Open-weight LLMs fine-tuned with Contrastive Preference Optimization (CPO) using this dataset achieved significant performance improvements, highlighting the benefits of heterogeneous trace generators.