Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916133823578112 |
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| author | Lee, Younghun Goldwasser, Dan Reese, Laura Schwab |
| author_facet | Lee, Younghun Goldwasser, Dan Reese, Laura Schwab |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14200 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models Lee, Younghun Goldwasser, Dan Reese, Laura Schwab Computation and Language 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. |
| title | Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.14200 |