Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences 事件

Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences arXiv:2606.07629v1 Announce Type: cross Abstract: Current approaches to aligning large language models (LLMs) aggregate diverse human preferences into a single reward signal, effectively optimizing for a hypothetical ``average user'' who represents no real person particularly well. This position paper argues that LLMs should learn personalized, individual preferences rather than aggregated ones. We show tha