Synthetic Contrastive Reasoning for Multi-Table Q&A
Multi-table question answering requires models to retrieve relevant evidence, link schemas, and perform compositional reasoning across relational tables. Existing multi-table Q&A resources typically provide questions and final answers but lack reasoning supervision that explains how answers are derived. To address this gap, we construct a synthetic contrastive reasoning-trace dataset for MMQA by generating validated positive traces and plausible negative traces with heterogeneous LLMs. We then use the resulting preference pairs to fine-tune open-weight LLMs with Contrastive Preference Optimization (CPO). Across Qwen3-14B, Mistral-8B, and Llama-3.1-8B, CPO achieves absolute average improve...
| Authors | Ankit Pratap Singh, Xin Su, Phillip Howard |
|---|---|
| Primary category | cs.AI |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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