EasyEdit2：一个用于编辑大型语言模型的易用引导框架
EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models
AI总结：
本文提出 EasyEdit2 框架，通过引导向量生成器与应用器实现无需修改参数即可控制大型语言模型行为的即插即用式引导，支持安全、情感等多种测试时干预，仅需单样本即可高效调整模型响应。
摘要：
在本文中，我们介绍了 EasyEdit2，这是一个旨在实现即插即用可调性以控制大型语言模型（LLM）行为的框架。EasyEdit2 支持广泛的测试时干预，包括安全性、情感、个性、推理模式、事实性和语言特征。与其前身不同，EasyEdit2 采用了专为无缝模型引导而设计的新架构。它包含关键模块，如引导向量生成器和引导向量应用器，能够自动生成并应用引导向量来影响模型行为，而无需修改其参数。EasyEdit2 的主要优势之一是易于使用——用户不需要广泛的技术知识。仅需一个示例，他们就能有效地引导和调整模型的响应，使精确控制既易于获取又高效。在实验中，我们报告了不同 LLM 上的模型引导性能，证明了这些技术的有效性。我们已在 GitHub 上发布了源代码，并附带了一个演示笔记本。此外，我们还提供了一个演示视频以便快速介绍。
英文摘要：
In this paper, we introduce EasyEdit2, a framework designed to enable plug-and-play adjustability for controlling Large Language Model (LLM) behaviors. EasyEdit2 supports a wide range of test-time interventions, including safety, sentiment, personality, reasoning patterns, factuality, and language features. Unlike its predecessor, EasyEdit2 features a new architecture specifically designed for seamless model steering. It comprises key modules such as the steering vector generator and the steering vector applier, which enable automatic generation and application of steering vectors to influence the model's behavior without modifying its parameters. One of the main advantages of EasyEdit2 is its ease of use-users do not need extensive technical knowledge. With just a single example, they can effectively guide and adjust the model's responses, making precise control both accessible and efficient. Empirically, we report model steering performance across different LLMs, demonstrating the effectiveness of these techniques. We have released the source code on GitHub at https://github.com/zjunlp/EasyEdit along with a demonstration notebook. In addition, we provide a demo video at https://www.youtube.com/watch?v=AkfoiPfp5rQ for a quick introduction.
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。
