The rise of generative artificial intelligence, particularly Large Language
Models (LLMs), has intensified the imperative to scrutinize fairness alongside
accuracy. Recent studies have begun to investigate fairness evaluations for
LLMs within domains such as recommendations. Given that personalization is an
intrinsic aspect of recommendation systems, its incorporation into fairness
assessments is paramount. Yet, the degree to which current fairness evaluation
frameworks account for personalization remains unclear. Our comprehensive
literature review aims to fill this gap by examining how existing frameworks
handle fairness evaluations of LLMs, with a focus on the integration of
personalization factors. Despite an exhaustive collection and analysis of
relevant works, we discovered that most evaluations overlook personalization, a
critical facet of recommendation systems, thereby inadvertently perpetuating
unfair practices. Our findings shed light on this oversight and underscore the
urgent need for more nuanced fairness evaluations that acknowledge
personalization. Such improvements are vital for fostering equitable
development within the AI community.

研究了已存在的公平评估框架处理语言生成人工智能模型（特别是大型语言模型）的方法，聚焦于如何整合个性化因素，并发现现有的评估往往忽视个性化，从而间接地延续了不公平的做法，强调了对个性化的认识和更细致的公平评估的迫切需要，以促进人工智能社区的公平发展。