A Comprehensive Evaluation framework of Alignment Techniques for LLMs

TL;DR

This paper presents a comprehensive, multi-dimensional evaluation framework for comparing various alignment techniques in Large Language Models, aiding systematic assessment and guiding future improvements.

Contribution

It introduces a unified evaluation framework that assesses alignment detection, quality, efficiency, and robustness across different LLM alignment methods.

Findings

Framework effectively differentiates strengths and weaknesses of alignment techniques.

Experiments reveal trade-offs between alignment quality and computational efficiency.

Insights guide future research in LLM alignment methods.

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.