In this paper, we propose a social robot capable of verbally interacting with children with Autism Spectrum Disorder (ASD). This communication is meant to teach perspective-taking using text generated using a Large Language Model (LLM) pipeline. The social robot NAO acts as a stimulator (verbally describes a social situation and asks a question), prompter (presents three options to choose from), and reinforcer (praises when the answer is correct). For the role of the stimulator, the social situation, questions, and options are generated using our LLM pipeline. We compare two approaches: GPT-2 + BART and GPT-2 + GPT-2, where the first GPT-2 common between the pipelines is used for unsupervised social situation generation. We use the SOCIALIQA dataset to fine-tune all of our LLM pipelines. We found that the GPT-2 + BART pipeline had a better BERTscore for generating the questions and the options by combining their individual loss functions. This observation was also consistent with the human evaluations. Lastly, the unsupervised generation of social situations was visualized using T-SNE plots, and the entire pipeline was evaluated for appropriateness for children with ASD by human experts.

翻译：本文提出一种能够与自闭症谱系障碍（ASD）儿童进行语言交互的社交机器人。该交互过程旨在利用大语言模型（LLM）流水线生成的文本，教授儿童进行观点采择。社交机器人NAO扮演三个角色：刺激器（口头描述社交情境并提出问题）、提示器（提供三个选项供选择）和强化器（回答正确时给予表扬）。在刺激器角色中，社交情境、问题及选项均由我们提出的LLM流水线生成。我们比较了两种方法：GPT-2+BART与GPT-2+GPT-2，其中两个流水线共用的首个GPT-2模块负责无监督社交情境生成。采用SOCIALIQA数据集对全部LLM流水线进行微调。研究发现，GPT-2+BART流水线通过合并各自损失函数，在生成问题与选项时取得了更优的BERTscore指标。该结果与人工评估结果一致。最后，通过T-SNE图对无监督生成的社交情境进行可视化，并由人类专家评估整个流水线对ASD儿童的适宜性。