解锁大型语言模型：应对心理健康领域数据稀缺与偏差挑战
Unlocking LLMs: Addressing Scarce Data and Bias Challenges in Mental Health
AI总结：
针对心理健康领域数据稀缺与偏差问题，构建专家标注的IC-AnnoMI数据集，利用ChatGPT生成对话并评估其情感推理能力，为MI社区提供数据集与LLM应用见解。
摘要：
大型语言模型（LLMs）在医疗分析中展现出有前景的能力，但面临幻觉、鹦鹉学舌和偏差表现等若干挑战。这些挑战在复杂、敏感和低资源领域尤为突出。因此，在本工作中，我们引入了IC-AnnoMI，一个基于AnnoMI构建的专家标注动机性访谈（MI）数据集，通过利用LLMs（特别是ChatGPT）生成上下文对话。IC-AnnoMI采用通过线索和定制信息精准设计的定向提示，考虑治疗风格（共情、反思）、上下文相关性和虚假语义变化。随后，对话由专家严格遵循动机性访谈技能编码（MISC）进行标注，聚焦于MI对话的心理和语言维度。我们通过建模新颖的分类任务，采用若干经典机器学习和当前最先进的Transformer方法，全面评估IC-AnnoMI数据集以及ChatGPT的情感推理能力和对领域复杂性的理解。最后，我们讨论了渐进式提示策略的效果以及增强数据在缓解IC-AnnoMI中表现出的偏差方面的作用。我们的贡献不仅为MI社区提供了一个全面的数据集，还为在监督环境中使用LLMs进行共情文本生成以支持对话治疗提供了宝贵见解。
英文摘要：
Large language models (LLMs) have shown promising capabilities in healthcare analysis but face several challenges like hallucinations, parroting, and bias manifestation. These challenges are exacerbated in complex, sensitive, and low-resource domains. Therefore, in this work we introduce IC-AnnoMI, an expert-annotated motivational interviewing (MI) dataset built upon AnnoMI by generating in-context conversational dialogues leveraging LLMs, particularly ChatGPT. IC-AnnoMI employs targeted prompts accurately engineered through cues and tailored information, taking into account therapy style (empathy, reflection), contextual relevance, and false semantic change. Subsequently, the dialogues are annotated by experts, strictly adhering to the Motivational Interviewing Skills Code (MISC), focusing on both the psychological and linguistic dimensions of MI dialogues. We comprehensively evaluate the IC-AnnoMI dataset and ChatGPT's emotional reasoning ability and understanding of domain intricacies by modeling novel classification tasks employing several classical machine learning and current state-of-the-art transformer approaches. Finally, we discuss the effects of progressive prompting strategies and the impact of augmented data in mitigating the biases manifested in IC-AnnoM. Our contributions provide the MI community with not only a comprehensive dataset but also valuable insights for using LLMs in empathetic text generation for conversational therapy in supervised settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
