Can Generic LLMs Help Analyze Child-Adult Interactions Involving Children with Autism in Clinical Observation?

Tiantian Feng, Anfeng Xu, Rimita Lahiri, Sudarsana Reddy Kadiri, Helen Tager-Flusberg, So Hyun Kim, Somer Bishop, Catherine Lord, Shrikanth Narayanan

Keywords: Autism, Large Language Model, Clinical NLP, Child-Adult Interaction

TL;DR: This paper explores the ability of LLM in understanding child-adult interaction in clinical observations involving children with autism.

Abstract: 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.

Submission Number: 48