Understanding the dynamics of counseling conversations is an important task,
yet it is a challenging NLP problem regardless of the recent advance of
Transformer-based pre-trained language models. This paper proposes a systematic
approach to examine the efficacy of domain knowledge and large language models
(LLMs) in better representing conversations between a crisis counselor and a
help seeker. We empirically show that state-of-the-art language models such as
Transformer-based models and GPT models fail to predict the conversation
outcome. To provide richer context to conversations, we incorporate
human-annotated domain knowledge and LLM-generated features; simple integration
of domain knowledge and LLM features improves the model performance by
approximately 15%. We argue that both domain knowledge and LLM-generated
features can be exploited to better characterize counseling conversations when
they are used as an additional context to conversations.

提出了一个系统的方法，用于检验领域知识和大型语言模型在代表危机辅导员与求助者之间的对话时的表现，结果表明领域知识和语言模型生成的特征能够更好地描述咨询对话。