Luciano Corro


2024

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Automatic Pair Construction for Contrastive Post-training
Canwen Xu | Corby Rosset | Ethan Chau | Luciano Corro | Shweti Mahajan | Julian McAuley | Jennifer Neville | Ahmed Awadallah | Nikhil Rao
Findings of the Association for Computational Linguistics: NAACL 2024

Alignment serves as an important step to steer large language models (LLMs) towards human preferences. In this paper, we propose an automatic way to construct contrastive data for LLM, using preference pairs from multiple models of varying strengths (e.g., InstructGPT, ChatGPT and GPT-4). We compare the contrastive techniques of SLiC and DPO to SFT baselines and find that DPO provides a step-function improvement even after continuing SFT saturates. We also explore a data curriculum learning scheme for contrastive post-training, which starts by learning from “easier” pairs and transitioning to “harder” ones, which further improves alignment. Finally, we scale up our experiments to train with more data and larger models like Orca. Remarkably, our automatic contrastive post-training further improves the performance of Orca, already a state-of-the-art instruction learning model tuned with GPT-4 outputs, to outperform ChatGPT.