Recent research shows that greater numbers of people are turning to Large Language Models (LLMs) for emotional support, and that people rate LLM responses as more empathic than human-written responses. We suggest a reason for this success: LLMs have learned and consistently deploy a well-liked template for expressing empathy. We develop a taxonomy of 10 empathic language "tactics" that include validating someone's feelings and paraphrasing, and apply this taxonomy to characterize the language that people and LLMs produce when writing empathic responses. Across a set of 2 studies comparing a total of n = 3,265 AI-generated (by six models) and n = 1,290 human-written responses, we find that LLM responses are highly formulaic at a discourse functional level. We discovered a template -- a structured sequence of tactics -- that matches between 83--90% of LLM responses (and 60--83\% in a held out sample), and when those are matched, covers 81--92% of the response. By contrast, human-written responses are more diverse. We end with a discussion of implications for the future of AI-generated empathy.

翻译：近期研究表明，越来越多的人转向大语言模型寻求情感支持，且用户认为模型生成的回应比人类撰写的回应更具共情力。我们认为这一成功源于：大语言模型已习得并持续运用一种广受欢迎的共情表达模板。我们构建了包含10种共情语言"策略"的分类体系（涵盖情感验证与释义重述等），并运用该体系刻画人类与语言模型在生成共情回应时的语言特征。通过两项对照研究（共涉及n=3,265条AI生成回应（来自6个模型）与n=1,290条人类撰写回应），发现语言模型的回应在话语功能层面具有高度套路化特征。我们识别出由策略组合构成的模板化结构——该模板能匹配83-90%的模型回应（在保留样本中为60-83%），且匹配后平均覆盖回应内容的81-92%。相较而言，人类撰写的回应更具多样性。最后，我们探讨了该发现对人工智能共情未来的启示。