To promote constructive discussion of controversial topics online, we propose
automatic reframing of disagreeing responses to signal receptiveness while
preserving meaning. Drawing on research from psychology, communications, and
linguistics, we identify six strategies for reframing. We automatically reframe
replies according to each strategy, using a dataset of Reddit comments and
replies. Through human-centered experiments, we find that the replies generated
with our framework are perceived to be significantly more receptive than the
original replies, as well as a generic receptiveness baseline. We analyze and
discuss the implications of our results and highlight applications to content
moderation. Overall, we illustrate how transforming receptiveness, a particular
social science construct, into a computational framework, can make LLM
generations more aligned with human perceptions.

通过从心理学、传播学和语言学的研究中汲取经验，我们提出了一种自动转述异议回应以传达接纳态度同时保持意义的方法，通过对 Reddit 评论和回应的数据集进行每种方法的自动转述，经过以人为中心的实验，我们发现我们的框架生成的回复被认为比原始回复以及通用接纳度基线更具接纳度，分析并讨论结果的含义，并强调对内容管理的应用，总体而言，我们说明了如何将接纳度这一特定的社会科学构造转化为计算框架，从而使 LLM 生成更符合人类感知。