@inproceedings{lee-etal-2024-towards,
title = "Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models",
author = "Lee, Younghun and
Goldwasser, Dan and
Reese, Laura Schwab",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-eacl.137",
pages = "2032--2047",
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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%0 Conference Proceedings
%T Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models
%A Lee, Younghun
%A Goldwasser, Dan
%A Reese, Laura Schwab
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F lee-etal-2024-towards
%X 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.
%U https://aclanthology.org/2024.findings-eacl.137
%P 2032-2047
Markdown (Informal)
[Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models](https://aclanthology.org/2024.findings-eacl.137) (Lee et al., Findings 2024)
ACL