Towards Understanding Counseling Conversations: Domain Knowledge and
  Large Language Models
Younghun Lee Dan Goldwasser Laura Schwab Reese
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.