Recent work on dialogue-based collaborative plan acquisition (CPA) has suggested that Theory of Mind (ToM) modelling can improve missing knowledge prediction in settings with asymmetric skill-sets and knowledge. Although ToM was claimed to be important for effective collaboration, its real impact on this novel task remains under-explored. By representing plans as graphs and by exploiting task-specific constraints we show that, as performance on CPA nearly doubles when predicting one's own missing knowledge, the improvements due to ToM modelling diminish. This phenomenon persists even when evaluating existing baseline methods. To better understand the relevance of ToM for CPA, we report a principled performance comparison of models with and without ToM features. Results across different models and ablations consistently suggest that learned ToM features are indeed more likely to reflect latent patterns in the data with no perceivable link to ToM. This finding calls for a deeper understanding of the role of ToM in CPA and beyond, as well as new methods for modelling and evaluating mental states in computational collaborative agents.

翻译：近期关于基于对话的协作计划获取（CPA）的研究表明，在技能集与知识不对称的场景中，心智理论（ToM）建模能够改善缺失知识预测。尽管ToM被认为对有效协作至关重要，但其在该新任务中的实际影响仍缺乏深入探究。通过将计划表示为图结构并利用任务特定约束，我们发现：当预测自身缺失知识的CPA性能近乎翻倍时，ToM建模带来的改进效果逐渐减弱。即使评估现有基线方法时，该现象依然存在。为更深入理解ToM对CPA的相关性，我们报告了含ToM特征与不含ToM特征模型的原理性性能对比。不同模型与消融研究的结果一致表明，习得的ToM特征更可能反映数据中与ToM无显著关联的潜在模式。这一发现要求我们更深入理解ToM在CPA及其他任务中的作用，同时需要开发新方法以建模和评估计算型协作代理的心理状态。