从大型语言模型中蒸馏共情能力
Distilling Empathy from Large Language Models
AI总结：
本文提出一种从大型语言模型向小型语言模型蒸馏共情能力的全面方法，通过两步微调和四组针对性共情提升提示词，使微调后的SLM在生成共情响应上以90%胜率显著优于基础模型。
摘要：
将大型语言模型（LLMs）的知识蒸馏到小型语言模型（SLMs）中，在保留LLMs能力和性能的同时减小模型规模，这一直在LLMs的普及中发挥了关键作用。由于SLMs比LLMs小得多，它们通常被用于人类交互频繁但资源高度受限的领域，例如智能手机。因此，确保共情能力（这种积极人类互动的基本方面，已经被灌输到LLMs中）在蒸馏后被SLMs保留下来，是至关重要的。在本文中，我们开发了一种全面的方法，用于将共情能力从LLMs有效地蒸馏到SLMs中。我们的方法具有一个两步微调过程，充分利用从LLMs蒸馏出的共情对话响应数据集。我们探索了超越基本直接提示的几种蒸馏方法，并提出了四组独特的提示词用于针对性的共情能力提升，以显著增强共情蒸馏过程。我们的评估表明，通过两步微调过程并使用由针对性共情提升提示词增强的蒸馏数据集进行微调的SLMs，在生成共情响应方面显著优于基础SLM，胜率达到90%。我们的针对性共情提升提示词在胜率上提高了10%，大幅优于基本直接提示方法。
英文摘要：
The distillation of knowledge from Large Language Models (LLMs) into Smaller Language Models (SLMs), preserving the capabilities and performance of LLMs while reducing model size, has played a key role in the proliferation of LLMs. Because SLMs are considerably smaller than LLMs, they are often utilized in domains where human interaction is frequent but resources are highly constrained, e.g., smart phones. Therefore, it is crucial to ensure that empathy, a fundamental aspect of positive human interactions, already instilled into LLMs, is retained by SLMs after distillation. In this paper, we develop a comprehensive approach for effective empathy distillation from LLMs into SLMs. Our approach features a two-step fine-tuning process that fully leverages datasets of empathetic dialogue responses distilled from LLMs. We explore several distillation methods beyond basic direct prompting and propose four unique sets of prompts for targeted empathy improvement to significantly enhance the empathy distillation process. Our evaluations demonstrate that SLMs fine-tuned through the two-step fine-tuning process with distillation datasets enhanced by the targeted empathy improvement prompts significantly outperform the base SLM at generating empathetic responses with a win rate of 90%. Our targeted empathy improvement prompts substantially outperform the basic direct prompting with a 10% improvement in win rate.
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。
