SYNTHEMPATHY：一种无需任何众包、使用LLM生成的可扩展共情语料库
SYNTHEMPATHY: A Scalable Empathy Corpus Generated Using LLMs Without Any Crowdsourcing
AI总结：
针对共情对话语料稀缺且众包成本高的问题，提出利用LLM生成10.5万条共情回应的SYNTHEMPATHY语料库，微调Mistral 7B后平均共情得分提升。
摘要：
先前研究表明，人类对表现出共情行为的语言模型接受度更高。尽管共情对于开发有用的对话智能体至关重要，但目前可用于微调LLM的包含共情对话的大型语料库却非常稀少。现有的少数语料库大多依赖众包来模拟共情对话，这一过程成本高昂、耗时，且难以扩展到更大的数据集。我们提出了一种数据生成框架，用于构建SYNTHEMPATHY，这是一个通过LLM生成编译而成的大型语料库，包含10.5万条针对现实生活情境的共情回应。基于我们的SYNTHEMPATHY语料库微调的Mistral 7B基础模型，其平均共情得分有所提升。
英文摘要：
Previous research has shown that humans are more receptive towards language models that that exhibit empathetic behavior. While empathy is essential for developing helpful dialogue agents, very few large corpora containing empathetic dialogues are available for fine-tune LLMs. The few existing corpora have largely relied on crowdsourcing to simulate empathetic conversations, a process that is expensive, time-consuming, and not scalable to larger datasets. We propose a data generation framework for developing SYNTHEMPATHY, a large corpus containing 105k empathetic responses to real-life situations compiled through LLM generation. A base Mistral 7B model fine-tuned on our SYNTHEMPATHY corpus exhibits an increase in the average empathy score.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
