The interest in Empathetic and Emotional Support conversations among the
public has significantly increased. To offer more sensitive and understanding
responses, leveraging commonsense knowledge has become a common strategy to
better understand psychological aspects and causality. However, such
commonsense inferences can be out of context and unable to predict upcoming
dialogue themes, resulting in responses that lack coherence and empathy. To
remedy this issue, we present Prophetic Commonsense Inference, an innovative
paradigm for inferring commonsense knowledge. By harnessing the capabilities of
Large Language Models in understanding dialogue and making commonsense
deductions, we train tunable models to bridge the gap between past and
potential future dialogues. Extensive experiments conducted on
EmpatheticDialogues and Emotion Support Conversation show that equipping
dialogue agents with our proposed prophetic commonsense inference significantly
enhances the quality of their responses.

通过利用大型语言模型的能力，在对话和运用常识推理方面训练可调节的模型，我们提出了先知性常识推理这一创新范式，它能够弥补常识推理过程中的语境不匹配和无法预测未来对话主题的问题，显著提升对话代理的回应质量。

加强具有先知性常识推理的共情和情感支持对话生成

Enhancing Empathetic and Emotion Support Dialogue Generation with  Prophetic Commonsense Inference

Large Language Models (LLM) like ChatGPT have significantly advanced in
recent years and are now being used by the general public. As more people
interact with these systems, improving our understanding of these black box
models is crucial, especially regarding their understanding of human
psychological aspects. In this work, we investigate their emotion perception
through the lens of appraisal and coping theory using the Stress and Coping
Process Questionaire (SCPQ). SCPQ is a validated clinical instrument consisting
of multiple stories that evolve over time and differ in key appraisal variables
such as controllability and changeability. We applied SCPQ to three recent LLMs
from OpenAI, davinci-003, ChatGPT, and GPT-4 and compared the results with
predictions from the appraisal theory and human data. The results show that
LLMs' responses are similar to humans in terms of dynamics of appraisal and
coping, but their responses did not differ along key appraisal dimensions as
predicted by the theory and data. The magnitude of their responses is also
quite different from humans in several variables. We also found that GPTs can
be quite sensitive to instruction and how questions are asked. This work adds
to the growing literature evaluating the psychological aspects of LLMs and
helps enrich our understanding of the current models.

通过评估应激与应对理论，本研究调查了大型语言模型对情绪感知的能力，并与人类数据进行了比较；结果显示了模型在评估和应对的动态方面类似于人类，但在某些维度上与预期不符，且其响应幅度与人类存在较大差异；此外，研究还发现模型对指令和提问方式非常敏感，从而丰富了对当前模型心理学方面的认识。

探究大型语言模型对情绪的感知 —— 基于评估理论的研究

Investigating Large Language Models' Perception of Emotion Using  Appraisal Theory

Large Language Models (LLMs) have recently showcased their remarkable
capacities, not only in natural language processing tasks but also across
diverse domains such as clinical medicine, legal consultation, and education.
LLMs become more than mere applications, evolving into assistants capable of
addressing diverse user requests. This narrows the distinction between human
beings and artificial intelligence agents, raising intriguing questions
regarding the potential manifestation of personalities, temperaments, and
emotions within LLMs. In this paper, we propose a framework, PsychoBench, for
evaluating diverse psychological aspects of LLMs. Comprising thirteen scales
commonly used in clinical psychology, PsychoBench further classifies these
scales into four distinct categories: personality traits, interpersonal
relationships, motivational tests, and emotional abilities. Our study examines
five popular models, namely \texttt{text-davinci-003}, ChatGPT, GPT-4,
LLaMA-2-7b, and LLaMA-2-13b. Additionally, we employ a jailbreak approach to
bypass the safety alignment protocols and test the intrinsic natures of LLMs.
We have made PsychoBench openly accessible via
https://github.com/CUHK-ARISE/PsychoBench.

该研究提出了一个评估 LLMs 不同心理方面的框架 PsychoBench，其中包括了四个明确分类的部分：人格特征、人际关系、动机测试和情绪能力。研究使用了五个常用模型进行测试，并采用了越狱方法绕过安全对齐协议，测试了 LLMs 的内在特性。