Abstract
As Large Language Models (LLMs) become increasingly integrated into real-world applications, ensuring their outputs align with human values and safety standards has become critical. The field has developed diverse alignment approaches including traditional fine-tuning methods (RLHF, instruction tuning), post-hoc correction systems, and inference-time interventions, each with distinct advantages and limitations. However, the lack of unified evaluation frameworks makes it difficult to systematically compare these paradigms and guide deployment decisions. This paper introduces a multi-dimensional evaluation of alignment techniques for LLMs, a comprehensive evaluation framework that provides a systematic comparison across all major alignment paradigms. Our framework assesses methods along four key dimensions: alignment detection, alignment quality, computational efficiency, and robustness. Through experiments across diverse base models and alignment strategies, we demonstrate the utility of our framework in identifying strengths and limitations of current state-of-the-art models, providing valuable insights for future research directions.
Abstract (translated)
随着大型语言模型（LLMs）在现实世界应用中的集成越来越深入，确保其输出与人类价值观和安全标准保持一致变得至关重要。该领域已经开发出了多种对齐方法，包括传统的微调方法（RLHF、指令微调）、事后修正系统以及推理时间干预措施等，每种方法都有各自的优势和局限性。然而，缺乏统一的评估框架使得系统地比较这些范式并指导部署决策变得困难。本文介绍了一种针对LLMs对齐技术的多维度评价方法，提供了一个全面的评估框架，用于在所有主要对齐范式之间进行系统的比较。我们的框架从四个方面评估各种方法：对齐检测、对齐质量、计算效率以及鲁棒性。通过在多种基础模型和对齐策略上的实验，我们展示了该框架在识别当前最先进模型的优势与局限方面的作用，并为未来的研究方向提供了有价值的见解。
URL
https://arxiv.org/abs/2508.09937
