May 6, 2024, 4:47 a.m. | Chuanbo Hu, Wenqi Li, Mindi Ruan, Xiangxu Yu, Lynn K. Paul, Shuo Wang, Xin Li

cs.CL updates on arXiv.org arxiv.org

arXiv:2405.01799v1 Announce Type: new
Abstract: Diagnosing language disorders associated with autism is a complex and nuanced challenge, often hindered by the subjective nature and variability of traditional assessment methods. Traditional diagnostic methods not only require intensive human effort but also often result in delayed interventions due to their lack of speed and specificity. In this study, we explored the application of ChatGPT, a state of the art large language model, to overcome these obstacles by enhancing diagnostic accuracy and profiling …

abstract arxiv assessment autism challenge chatgpt cs.ai cs.cl diagnostic features human language nature type

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