This paper introduces universal fairness, a clinically grounded definition that reframes fairness as maximizing subgroup-aware diagnostic performance under attribute-conditioned health disparities, and proposes MAPPE, a training-free minimax prompt optimization framework that theoretically promotes universal fairness.
Jiaming Zhang, Yu-Yuan Li, Xiaohua Feng et al.· Proceedings of the 32nd ACM...· 0 citations
ReSOT is proposed, a unified framework that Re-balances Semantic ID learning via Optimal Transport for GR and provides a principled tokenization scheme that preserves relational structure while assigning codes in a collision-aware and semantics-consistent manner.
Renwu Geng, Yi-Ming Xu, Fengxin Li et al.· Proceedings of the 32nd ACM...· 0 citations
Generative recommendation (GR) reformulates sequential recommendation as an autoregressive generation problem, where items are represented as discrete semantic IDs. However, learning effective item tokenization is critical yet remains challenging. Most existing methods optimize tokenization in a point-wise or heuristic...
Renwu Geng, Yiming Xu, Fengxin Li et al.· Proceedings of the 32nd ACM...· 0 citations
Large Language Models (LLMs) have shown strong potential in medical applications such as question answering and clinical prediction. % Despite their growing adoption, fairness in LLMs for medicine remains underexplored, largely due to the mismatch between conventional fairness constraints and the clinically meaningful...
Jiaming Zhang, Yuyuan Li, Xiaohua Feng et al.· Proceedings of the 32nd ACM...· 0 citations
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