Large language models (LLMs) have garnered significant attention in recent years due to their impressive performance. While considerable research has evaluated these models from various perspectives, the extent to which LLMs can perform implicit and explicit emotion retrieval remains largely unexplored. To address this gap, this study investigates LLMs' emotion retrieval capabilities in commonsense. Through extensive experiments involving multiple models, we systematically evaluate the ability of LLMs on emotion retrieval. Specifically, we propose a supervised contrastive probing method to verify LLMs' performance for implicit and explicit emotion retrieval, as well as the diversity of the emotional events they retrieve. The results offer valuable insights into the strengths and limitations of LLMs in handling emotion retrieval.

翻译：近年来，大语言模型（LLMs）因其卓越的性能表现而受到广泛关注。尽管已有大量研究从不同角度对这些模型进行评估，但LLMs在多大程度上能够执行隐式与显式情感检索，在很大程度上仍未得到充分探索。为填补这一空白，本研究探讨了LLMs在常识情境下的情感检索能力。通过对多个模型进行大量实验，我们系统评估了LLMs在情感检索任务上的表现。具体而言，我们提出了一种监督对比探测方法，以验证LLMs在隐式与显式情感检索方面的性能，以及其检索到的情感事件的多样性。研究结果为理解LLMs在处理情感检索任务时的优势与局限性提供了重要见解。