通用大模型能有效分析自闭症儿童临床互动，辅助评估其语言与行为表现。
Can Generic LLMs Help Analyze Child-adult Interactions Involving Children with Autism in Clinical Observation?
- 用通用大模型分析自闭症儿童与成人对话，识别关键语句与互动模式。
- 在四个任务中表现优于非专家人类，可准确识别语言能力与参与活动。
- 适合临床评估辅助、教育干预设计及研究者快速分析长期观察数据。
大型语言模型（LLMs）在理解人类交流方面展现出巨大潜力，但在包含儿童的互动场景，尤其是临床环境中的应用仍较少被探索。本文评估通用大模型在涉及自闭症谱系障碍（ASD）儿童的临床观察中分析亲子二元互动的能力。具体考察四类任务：区分儿童-成人话语、预测参与活动、识别语言技能及理解临床相关特质。结果表明，通用大模型能够高效处理长而复杂的临床观察对话，性能常超过非专业人类评估者。模型具备分割感兴趣互动、辅助语言能力评估、识别参与活动及提供临床背景支持的潜力。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have shown significant potential in understanding human communication and interaction. However, their performance in the domain of child-inclusive interactions, including in clinical settings, remains less explored. In this work, we evaluate generic LLMs' ability to analyze child-adult dyadic interactions in a clinically relevant context involving children with ASD. Specifically, we explore LLMs in performing four tasks: classifying child-adult utterances, predicting engaged activities, recognizing language skills and understanding traits that are clinically relevant. Our evaluation shows that generic LLMs are highly capable of analyzing long and complex conversations in clinical observation sessions, often surpassing the performance of non-expert human evaluators. The results show their potential to segment interactions of interest, assist in language skills evaluation, identify engaged activities, and offer clinical-relevant context for assessments.
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