EasySteer: 一种高绩效且可扩展的LLM引导统一框架
EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering
AI总结：
EasySteer通过模块化架构和预计算引导向量，实现高效且可扩展的LLM引导，提升推理效率并支持多种应用场景。
摘要：
大型语言模型（LLM）引导已作为一种通过针对性操纵隐藏状态来在推理时间控制模型行为的有前景范式出现，提供了一种轻量级替代昂贵重新训练的方案。然而，现有的引导框架存在关键限制：计算效率低下、扩展性有限和功能受限，这阻碍了研究进展和实际部署。我们提出了EasySteer，一种基于vLLM的高绩效、可扩展的LLM引导统一框架。我们的系统具有模块化架构，具有可插拔的分析型和学习型方法接口，细粒度参数控制，预计算的八个应用领域的引导向量，以及一个交互式演示系统。通过深入集成vLLM的优化推理引擎，EasySteer在现有框架上实现了10.8-22.3倍的速度提升。广泛的实验展示了其在抑制过度思考、减少幻觉等关键应用中的有效性。EasySteer将引导从研究技术转变为可生产的生产能力，为可部署、可控的语言模型建立了关键基础设施。
英文摘要：
Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted manipulation of hidden states, offering a lightweight alternative to expensive retraining. However, existing steering frameworks suffer from critical limitations: computational inefficiency, limited extensibility, and restricted functionality that hinder both research progress and practical deployment. We present EasySteer, a unified framework for high-performance, extensible LLM steering built on vLLM. Our system features modular architecture with pluggable interfaces for both analysis-based and learning-based methods, fine-grained parameter control, pre-computed steering vectors for eight application domains, and an interactive demonstration system. Through deep integration with vLLM's optimized inference engine, EasySteer achieves 10.8-22.3$\times$ speedup over existing frameworks. Extensive experiments demonstrate its effectiveness in overthinking mitigation, hallucination reduction, and other key applications. EasySteer transforms steering from research technique to production-ready capability, establishing critical infrastructure for deployable, controllable language models.
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