Empathy serves as a cornerstone in enabling prosocial behaviors, and can be
evoked through sharing of personal experiences in stories. While empathy is
influenced by narrative content, intuitively, people respond to the way a story
is told as well, through narrative style. Yet the relationship between empathy
and narrative style is not fully understood. In this work, we empirically
examine and quantify this relationship between style and empathy using LLMs and
large-scale crowdsourcing studies. We introduce a novel, theory-based taxonomy,
HEART (Human Empathy and Narrative Taxonomy) that delineates elements of
narrative style that can lead to empathy with the narrator of a story. We
establish the performance of LLMs in extracting narrative elements from HEART,
showing that prompting with our taxonomy leads to reasonable, human-level
annotations beyond what prior lexicon-based methods can do. To show empirical
use of our taxonomy, we collect a dataset of empathy judgments of stories via a
large-scale crowdsourcing study with N=2,624 participants. We show that
narrative elements extracted via LLMs, in particular, vividness of emotions and
plot volume, can elucidate the pathways by which narrative style cultivates
empathy towards personal stories. Our work suggests that such models can be
used for narrative analyses that lead to human-centered social and behavioral
insights.

借助 LLMs 和大规模众包研究，我们实证检验并量化了故事叙述风格和共情之间的关系，引入了 HEART（人类共情和叙事分类系统）这一基于理论的新分类系统，证明了 LLMs 在从 HEART 中提取叙事要素方面的优势，并通过大规模众包研究收集到的情感判断数据集表明，通过 LLMs 提取的叙事要素，特别是情感的生动性和情节的音量，可以阐明叙事风格在培养对个人故事的共情中的路径。我们的工作表明，这样的模型可以用于引导以人为中心的社会和行为洞察的叙事分析。

HEART-felt Narratives: 追踪 LLM 中个人故事中的同理心和叙事风格

HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal  Stories with LLMs

Most research on natural language processing treats bias as an absolute
concept: Based on a (probably complex) algorithmic analysis, a sentence, an
article, or a text is classified as biased or not. Given the fact that for
humans the question of whether a text is biased can be difficult to answer or
is answered contradictory, we ask whether an "absolute bias classification" is
a promising goal at all. We see the problem not in the complexity of
interpreting language phenomena but in the diversity of sociocultural
backgrounds of the readers, which cannot be handled uniformly: To decide
whether a text has crossed the proverbial line between non-biased and biased is
subjective. By asking "Is text X more [less, equally] biased than text Y?" we
propose to analyze a simpler problem, which, by its construction, is rather
independent of standpoints, views, or sociocultural aspects. In such a model,
bias becomes a preference relation that induces a partial ordering from least
biased to most biased texts without requiring a decision on where to draw the
line. A prerequisite for this kind of bias model is the ability of humans to
perceive relative bias differences in the first place. In our research, we
selected a specific type of bias in argumentation, the stance bias, and
designed a crowdsourcing study showing that differences in stance bias are
perceptible when (light) support is provided through training or visual aid.

探讨了传统自然语言处理方法中的绝对偏见分类目标是否可取，并尝试提供了一种基于相对偏见分析的模型。作者以争论中的立场偏见为案例进行众包实验，证明有较弱的人工干预的情况下可以感知文本之间的相对偏见区别。