Advances in the performance of large language models (LLMs) have led some
researchers to propose the emergence of theory of mind (ToM) in artificial
intelligence (AI). LLMs can attribute beliefs, desires, intentions, and
emotions, and they will improve in their accuracy. Rather than employing the
characteristically human method of empathy, they learn to attribute mental
states by recognizing linguistic patterns in a dataset that typically do not
include that individual. We ask whether LLMs' inability to empathize precludes
them from honoring an individual's right to be an exception, that is, from
making assessments of character and predictions of behavior that reflect
appropriate sensitivity to a person's individuality. Can LLMs seriously
consider an individual's claim that their case is different based on internal
mental states like beliefs, desires, and intentions, or are they limited to
judging that case based on its similarities to others? We propose that the
method of empathy has special significance for honoring the right to be an
exception that is distinct from the value of predictive accuracy, at which LLMs
excel. We conclude by considering whether using empathy to consider exceptional
cases has intrinsic or merely practical value and we introduce conceptual and
empirical avenues for advancing this investigation.

大语言模型（LLMs）的性能提升引发了研究者提出人工智能（AI）中心理理论（ToM）的出现。LLMs 能够赋予信念、欲望、意图和情感，并在准确性方面不断提高。我们探讨了 LLMs 无法产生共情是否妨碍了它们尊重个体作为例外的权利，即从个体性角度对人物性格和行为的评估和预测是否能够反映出适当的敏感性。我们提出了共情方法在尊重例外权利方面具有特殊意义，这与 LLMs 擅长的预测准确性价值不同。最后，我们考虑了使用共情来考虑例外情况的内在价值或实践价值，并介绍了推进这一调查的概念和实证途径。