Recent advances in Large Language Models (LLMs) have sparked wide interest in validating and comprehending the human-like cognitive-behavioral traits LLMs may have. These cognitive-behavioral traits include typically Attitudes, Opinions, Values (AOV). However, measuring AOV embedded within LLMs remains opaque, and different evaluation methods may yield different results. This has led to a lack of clarity on how different studies are related to each other and how they can be interpreted. This paper aims to bridge this gap by providing an overview of recent works on the evaluation of AOV in LLMs. Moreover, we survey related approaches in different stages of the evaluation pipeline in these works. By doing so, we address the potential and challenges with respect to understanding the model, human-AI alignment, and downstream application in social sciences. Finally, we provide practical insights into evaluation methods, model enhancement, and interdisciplinary collaboration, thereby contributing to the evolving landscape of evaluating AOV in LLMs.

翻译：大型语言模型（LLMs）的最新进展引发了学界对其可能具备的类人认知行为特质进行验证与理解的广泛兴趣。这些认知行为特质通常包括态度、观点与价值观（AOV）。然而，对LLMs中内嵌AOV的测量仍不透明，且不同的评估方法可能产生不同结果。这导致不同研究之间的关联性及其解释方式缺乏清晰界定。本文旨在通过综述近期关于LLMs中AOV评估的研究来弥合这一差距。此外，我们系统梳理了这些研究中评估流程不同阶段的相关方法。藉此，我们探讨了在模型理解、人机对齐及社会科学下游应用方面存在的潜力与挑战。最后，我们为评估方法、模型增强及跨学科合作提供了实践性见解，从而为不断发展的LLMs中AOV评估研究领域作出贡献。