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Preference Alignment, with Reference Mismatch, and without Reference Models

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If you have a question about this talk, please contact Suchir Salhan .

Abstract: In this talk, I’ll cover two recent papers for preference alignment: Odds-Ratio Preference Optimisation (ORPO, EMNLP 2024 ), discussing the role of the reference model for preference alignment (e.g. DPO , RLHF), and Margin-aware Preference Optimization (under review @ CVPR ), thinking about the risks of reference mismatch: where the preference alignment data has features diverging from the reference model.

Bio:
James is Assistant Professor at the KAIST Graduate School of AI, South Korea, working on large-scale and knowledge-intensive natural language understanding. James recently completed his PhD at the University of Cambridge where he developed models and methods for automated fact verification and correction.

[1] https://aclanthology.org/2024.emnlp-main.626/ [2] https://arxiv.org/pdf/2406.06424

This talk is part of the NLIP Seminar Series series.

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