Constructs synthetic contrastive reasoning-trace dataset for MMQA via heterogeneous LLMs and reports 9.7-16.3% absolute gains from CPO fine-tuning over standard SFT across three open LLMs.
InProceedings of the 63rd An- nual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 21223– 21261
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Synthetic Contrastive Reasoning for Multi-Table Q&A
Constructs synthetic contrastive reasoning-trace dataset for MMQA via heterogeneous LLMs and reports 9.7-16.3% absolute gains from CPO fine-tuning over standard SFT across three open LLMs.