Having the ability to empathize is crucial for accurately representing human
behavior during conversations. Despite numerous research aim to improve the
cognitive capability of models by incorporating external knowledge, there has
been limited attention on the sensible and rational expression of the
conversation itself, which are crucial components of the cognitive empathy.
Guided by self-presentation theory in sociology, we have designed an innovative
categorical approach that segregates historical dialogues into sensible and
rational sentences and subsequently elucidate the context through the designed
attention mechanism. However, the rational information within the conversation
is restricted and the external knowledge used in previous methods have
limitations of semantic contradiction and narrow vision field. Considering the
impressive performance of LLM in the domain of intelligent agent. We employ
LLaMA2-70b as a rational brain to analyze the profound logical information
maintained in conversations, which assists the model assessing the balance of
sensibility and rationality to produce quality empathetic responses.
Experimental evaluations demonstrate that our method outperforms other
comparable methods on both automatic and human evaluations.

使用分类方法将历史对话分成合理和合理的句子，并通过设计的注意机制阐明上下文，以分析对话中维持的逻辑信息，以帮助模型评估合理与理性的平衡，以产生优质的共情回应。

理性感知：由自我呈现理论引导的 LLM 增强型共情响应生成

Rational Sensibility: LLM Enhanced Empathetic Response Generation Guided  by Self-presentation Theory

Measuring empathy in conversation can be challenging, as empathy is a complex
and multifaceted psychological construct that involves both cognitive and
emotional components. Human evaluations can be subjective, leading to
inconsistent results. Therefore, there is a need for an automatic method for
measuring empathy that reduces the need for human evaluations. In this paper,
we proposed a novel approach EMP-EVAL, a simple yet effective automatic empathy
evaluation method. The proposed technique takes the influence of Emotion,
Cognitive and Emotional empathy. To the best knowledge, our work is the first
to systematically measure empathy without the human-annotated provided scores.
Experimental results demonstrate that our metrics can correlate with human
preference, achieving comparable results with human judgments.

本文提出了一种名为 EMP-EVAL 的自动衡量共情的方法，该方法结合了情感、认知和情感共情，能够减少人工评估的需求并且与人类判断具有可比性。实验结果表明，我们的指标可以与人类喜好相关联。

EMP-EVAL：一个在开放域对话中评估情感共鸣的框架

EMP-EVAL: A Framework for Measuring Empathy in Open Domain Dialogues

We review the state of research on empathy in natural language processing and
identify the following issues: (1) empathy definitions are absent or abstract,
which (2) leads to low construct validity and reproducibility. Moreover, (3)
emotional empathy is overemphasized, skewing our focus to a narrow subset of
simplified tasks. We believe these issues hinder research progress and argue
that current directions will benefit from a clear conceptualization that
includes operationalizing cognitive empathy components. Our main objectives are
to provide insight and guidance on empathy conceptualization for NLP research
objectives and to encourage researchers to pursue the overlooked opportunities
in this area, highly relevant, e.g., for clinical and educational sectors.

深入调查自然语言处理对同理心的影响，发现缺乏明确的同理心定义与过度强调情感同理心导致构建有效性与可重复性不足，提出需要以认知同理心为核心的定义来指导同理心在 NLP 研究领域的应用，并探寻该领域的被忽视的机遇。