使用大型语言模型检索隐式和显式情感事件
Retrieving Implicit and Explicit Emotional Events Using Large Language Models
AI总结：
针对LLMs在隐式和显式情感检索能力未充分探索的问题，本研究通过多模型实验及提出的有监督对比探测方法，系统评估其在常识领域的情感检索性能与检索事件多样性，揭示了LLMs的优势与局限性。
摘要：
近年来，大型语言模型（LLMs）因其出色的性能备受关注。尽管已有大量研究从不同角度对这些模型进行了评估，但LLMs在执行隐式和显式情感检索方面的能力仍未得到充分探索。为填补这一空白，本研究在常识领域对LLMs的情感检索能力展开了调查。通过涉及多个模型的大量实验，我们系统评估了LLMs在情感检索方面的能力。具体而言，我们提出了一种有监督对比探测方法，以验证LLMs在隐式和显式情感检索中的性能，以及它们所检索情感事件的多样性。研究结果为LLMs在处理情感检索任务中的优势与局限性提供了有价值的见解。
英文摘要：
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.
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