Personalized text generation presents a specialized mechanism for delivering
content that is specific to a user's personal context. While the research
progress in this area has been rapid, evaluation still presents a challenge.
Traditional automated metrics such as BLEU and ROUGE primarily measure lexical
similarity to human-written references, and are not able to distinguish
personalization from other subtle semantic aspects, thus falling short of
capturing the nuances of personalized generated content quality. On the other
hand, human judgments are costly to obtain, especially in the realm of
personalized evaluation. Inspired by these challenges, we explore the use of
large language models (LLMs) for evaluating personalized text generation, and
examine their ability to understand nuanced user context. We present AuPEL, a
novel evaluation method that distills three major semantic aspects of the
generated text: personalization, quality and relevance, and automatically
measures these aspects. To validate the effectiveness of AuPEL, we design
carefully controlled experiments and compare the accuracy of the evaluation
judgments made by LLMs versus that of judgements made by human annotators, and
conduct rigorous analyses of the consistency and sensitivity of the proposed
metric. We find that, compared to existing evaluation metrics, AuPEL not only
distinguishes and ranks models based on their personalization abilities more
accurately, but also presents commendable consistency and efficiency for this
task. Our work suggests that using LLMs as the evaluators of personalized text
generation is superior to traditional text similarity metrics, even though
interesting new challenges still remain.

个性化文本生成采用了一种专门的机制来提供与用户个人背景相关的内容，并且尽管这一领域的研究进展很快，但仍存在着评估方面的挑战。本研究提出了 AuPEL 作为一种新颖的评估方法，通过大型语言模型对个性化文本生成进行评估，用以满足人工评估的高成本和传统文本相似度度量方法的不足。实验证明，与现有的评估指标相比，AuPEL 不仅可以更准确地区分和排名模型的个性化能力，而且在此任务中表现出令人称赞的一致性和高效性。