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.
@article{arxiv.2508.09937,
title = {A Comprehensive Evaluation framework of Alignment Techniques for LLMs},
author = {Muneeza Azmat and Momin Abbas and Maysa Malfiza Garcia de Macedo and Marcelo Carpinette Grave and Luan Soares de Souza and Tiago Machado and Rogerio A de Paula and Raya Horesh and Yixin Chen and Heloisa Caroline de Souza Pereira Candello and Rebecka Nordenlow and Aminat Adebiyi},
journal= {arXiv preprint arXiv:2508.09937},
year = {2025}
}