PerQ：多语言文本个性化质量的高效评估
PerQ: Efficient Evaluation of Multilingual Text Personalization Quality
AI总结：
针对现有文本个性化质量评估依赖多模型元评估成本高的问题，本文提出高效评估方法PerQ，经大小模型生成能力对比验证其实用性，可降低资源消耗。
摘要：
由于目前没有可用于评估文本特定维度（如个性化质量）的指标，研究人员往往仅依赖大语言模型对这类文本进行元评估。受单个语言模型内部偏差的影响，学界建议使用多个模型联合评估，这直接推高了此类元评估的成本。本文提出了一种计算高效的方法，名为PerQ，用于评估（由语言模型生成的）给定文本的个性化质量。针对大、小语言模型生成能力开展的对比案例研究表明，该指标具备科研实用性，能有效减少资源浪费。
英文摘要：
Since no metrics are available to evaluate specific aspects of a text, such as its personalization quality, the researchers often rely solely on large language models to meta-evaluate such texts. Due to internal biases of individual language models, it is recommended to use multiple of them for combined evaluation, which directly increases costs of such meta-evaluation. In this paper, a computationally efficient method for evaluation of personalization quality of a given text (generated by a language model) is introduced, called PerQ. A case study of comparison of generation capabilities of large and small language models shows the usability of the proposed metric in research, effectively reducing the waste of resources.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
