  In-context learning (ICL) achieves remarkable performance in various domains
such as knowledge acquisition, commonsense reasoning, and semantic
understanding. However, its performance significantly deteriorates for emotion
detection tasks, especially fine-grained emotion recognition. The underlying
reasons for this remain unclear. In this paper, we identify the reasons behind
ICL's poor performance from the perspective of prototype theory and propose a
method to address this issue. Specifically, we conduct extensive pilot
experiments and find that ICL conforms to the prototype theory on fine-grained
emotion recognition. Based on this theory, we uncover the following
deficiencies in ICL: (1) It relies on prototypes (example-label pairs) that are
semantically similar but emotionally inaccurate to predict emotions. (2) It is
prone to interference from irrelevant categories, affecting the accuracy and
robustness of the predictions. To address these issues, we propose an Emotion
Context Learning method (E-ICL) on fine-grained emotion recognition. E-ICL
relies on more emotionally accurate prototypes to predict categories by
referring to emotionally similar examples with dynamic labels. Simultaneously,
E-ICL employs an exclusionary emotion prediction strategy to avoid interference
from irrelevant categories, thereby increasing its accuracy and robustness.
Note that the entire process is accomplished with the assistance of a
plug-and-play emotion auxiliary model, without additional training. Experiments
on the fine-grained emotion datasets EDOS, Empathetic-Dialogues,
EmpatheticIntent, and GoEmotions show that E-ICL achieves superior emotion
prediction performance. Furthermore, even when the emotion auxiliary model used
is lower than 10% of the LLMs, E-ICL can still boost the performance of LLMs by
over 4% on multiple datasets.
