In many literary texts, emotions are indirectly conveyed through descriptions
of actions, facial expressions, and appearances, necessitating emotion
inference for narrative understanding. In this paper, we introduce K-Act2Emo, a
Korean commonsense knowledge graph (CSKG) comprising 1,900 indirect emotional
expressions and the emotions inferable from them. We categorize reasoning types
into inferences in positive situations, inferences in negative situations, and
inferences when expressions do not serve as emotional cues. Unlike existing
CSKGs, K-Act2Emo specializes in emotional contexts, and experimental results
validate its effectiveness for training emotion inference models.
Significantly, the BART-based knowledge model fine-tuned with K-Act2Emo
outperforms various existing Korean large language models, achieving
performance levels comparable to GPT-4 Turbo.

介绍了 K-Act2Emo，一个包含 1,900 个间接情绪表达及其可推测的情绪的韩国常识知识图谱，将推理类型分为正面情境推理、负面情境推理和表情不起感情作用的推理，与现有的知识图谱不同，K-Act2Emo 专注于情绪上下文，并通过实验证明其在训练情绪推理模型方面的有效性。该基于 BART 模型并经过 K-Act2Emo 微调的知识模型在性能上优于各种现有的韩国大语言模型，达到了与 GPT-4 Turbo 可比较的水平。

K-Act2Emo：韩语基础知识图谱用于间接情感表达

K-Act2Emo: Korean Commonsense Knowledge Graph for Indirect Emotional  Expression

How humans infer discrete emotions is a fundamental research question in the
field of psychology. While conceptual knowledge about emotions (emotion
knowledge) has been suggested to be essential for emotion inference, evidence
to date is mostly indirect and inconclusive. As the large language models
(LLMs) have been shown to support effective representations of various human
conceptual knowledge, the present study further employed artificial neurons in
LLMs to investigate the mechanism of human emotion inference. With artificial
neurons activated by prompts, the LLM (RoBERTa) demonstrated a similar
conceptual structure of 27 discrete emotions as that of human behaviors.
Furthermore, the LLM-based conceptual structure revealed a human-like reliance
on 14 underlying conceptual attributes of emotions for emotion inference. Most
importantly, by manipulating attribute-specific neurons, we found that the
corresponding LLM's emotion inference performance deteriorated, and the
performance deterioration was correlated to the effectiveness of
representations of the conceptual attributes on the human side. Our findings
provide direct evidence for the emergence of emotion knowledge representation
in large language models and suggest its casual support for discrete emotion
inference.

本研究利用大型语言模型（LLMs）的人工神经元研究人类情感推断机制，发现 LLMs 基于情感知识的概念结构与人类行为中的离散情感的结构相似，且它基于 14 个概念属性的情感推断依赖程度与人类依赖程度相近，这为情感知识在大型语言模型中的出现提供了直接证据，并建议其对离散情感推断至关重要。

大型语言模型中出现的人类情感知识表示并支持离散情感推断

Human Emotion Knowledge Representation Emerges in Large Language Model and Supports Discrete Emotion Inference

The need for emotional inference from text continues to diversify as more and
more disciplines integrate emotions into their theories and applications. These
needs include inferring different emotion types, handling multiple languages,
and different annotation formats. A shared model between different
configurations would enable the sharing of knowledge and a decrease in training
costs, and would simplify the process of deploying emotion recognition models
in novel environments. In this work, we study how we can build a single model
that can transition between these different configurations by leveraging
multilingual models and Demux, a transformer-based model whose input includes
the emotions of interest, enabling us to dynamically change the emotions
predicted by the model. Demux also produces emotion embeddings, and performing
operations on them allows us to transition to clusters of emotions by pooling
the embeddings of each cluster. We show that Demux can simultaneously transfer
knowledge in a zero-shot manner to a new language, to a novel annotation format
and to unseen emotions. Code is available at
this https URL .

本文研究了如何使用多语言模型和 Demux 来建立一个能够在不同配置之间转换的单一情绪识别模型，其中 Demux 能够预测多种情绪，并在它们的情感嵌入中进行操作，从而允许我们动态地更改模型预测的情绪群集。我们表明，Demux 能够在零样本学习的方式下转移知识到新的语言、新的标注格式以及未见过的情感。