Large Language Models (LLMs) have demonstrated surprising performance on many
tasks, including writing supportive messages that display empathy. Here, we had
these models generate empathic messages in response to posts describing common
life experiences, such as workplace situations, parenting, relationships, and
other anxiety- and anger-eliciting situations. Across two studies (N=192, 202),
we showed human raters a variety of responses written by several models (GPT4
Turbo, Llama2, and Mistral), and had people rate these responses on how
empathic they seemed to be. We found that LLM-generated responses were
consistently rated as more empathic than human-written responses. Linguistic
analyses also show that these models write in distinct, predictable ``styles",
in terms of their use of punctuation, emojis, and certain words. These results
highlight the potential of using LLMs to enhance human peer support in contexts
where empathy is important.

大型语言模型（LLMs）在许多任务中表现出了令人惊讶的性能，包括撰写表达共情的支持性信息。我们在这里让这些模型生成对描述常见生活经历的帖子的共情信息，例如职场环境、育儿、人际关系和其他引发焦虑和愤怒的情境。通过两项研究（N=192，202），我们向人类评估者展示了几个模型（GPT4 Turbo、Llama2 和 Mistral）生成的各种回应，并要求他们评估这些回应的共情程度。我们发现 LLM 生成的回应在共情程度上一直被评为比人工撰写的回应更具共情性。语言分析还表明，这些模型在标点符号、表情符号和某些词语的使用方面具有独特、可预测的 “风格”。这些结果凸显了在强调共情的情境中利用 LLMs 提升人类同伴支持的潜力。