Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models

• URL: http://arxiv.org/abs/2402.14200v1
• Date: Thu, 22 Feb 2024 01:02:37 GMT
• Title: Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models
• Authors: Younghun Lee, Dan Goldwasser, Laura Schwab Reese
• Abstract summary: This paper proposes a systematic approach to examine the efficacy of domain knowledge and large language models (LLMs) in better representing counseling conversations. We empirically show that state-of-the-art language models such as Transformer-based models and GPT models fail to predict the conversation outcome.
• Score: 22.588557390720236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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