@misc{gueorguieva2026ai,
author = {Gueorguieva, Emma S. and Zhan, Hongli and Suh, Jina and Hernandez, Javier and Lau, Tatiana and Li, Junyi Jessy and Ong, Desmond C.},
title = {AI generates well-liked but templatic empathic responses},
howpublished = {arXiv},
year = {2026},
month = {April},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/ai-generates-well-liked-but-templatic-empathic-responses/},
}