Evaluating alignment in large language models: a review of methodologies

As artificial intelligence systems become more complex and widely adopted, ensuring their alignment with human values and goals is essential to prevent unintended harm. This paper reviews four primary methodologies for evaluating alignment in Large Language Models (LLMs): human feedback, adversarial testing by domain experts, AI red teaming, and the constitutional approach to AI safety. I examine the strengths, limitations, and practical applications of each approach, highlighting critical challenges such as detecting deceptive behavior (e.g., “AI sleeper agents”) and the ethical risks of adversarial training. Additionally, this paper explores the relationship between alignment and accountability, addressing the legal and ethical questions that arise as AI systems are deployed in real-world contexts. I outline future research directions in this rapidly evolving field to support the safe and ethical development of AI systems. This study aims to provide researchers and practitioners with a structured overview of current LLM testing methodologies and insights into areas needing further exploration.