用对比学习方法评估大模型隐含与显性情绪事件的识别能力
Retrieving Implicit and Explicit Emotional Events Using Large Language Models
- 提出监督对比探针法，区分模型对隐含与显性情绪的捕捉能力
- 多模型实验表明大模型在情绪事件检索上存在明显差异
- 揭示大模型在常识情绪理解中的优势与局限，适合情绪计算研究者参考
近年来，大语言模型（LLMs）因其卓越表现受到广泛关注。尽管已有大量研究从不同角度评估这些模型，但其在常识情境下对隐含与显性情绪事件的检索能力仍鲜有探索。为填补这一空白，本研究系统评估了多种大模型在情绪事件检索上的表现。具体而言，我们提出一种监督对比探针方法，用于验证大模型在隐含与显性情绪检索上的能力及其所生成情绪事件的多样性。实验结果为理解大模型在情绪检索任务中的优劣提供了重要洞察。
原文摘要 · Abstract (English)
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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