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Title: Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models
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.  more » « less
Award ID(s):
2048001
PAR ID:
10505820
Author(s) / Creator(s):
; ;
Editor(s):
Yvette Graham, Matthew Purver
Publisher / Repository:
Association for Computational Linguistics
Date Published:
Journal Name:
Findings of the Association for Computational Linguistics: EACL
Page Range / eLocation ID:
2032–2047
Format(s):
Medium: X
Location:
St. Julian’s, Malta
Sponsoring Org:
National Science Foundation
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