Recent advancements in Large Language Models (LLMs) have heightened concerns about their potential misalignment with human values. However, evaluating their grasp of these values is complex due to their intricate and adaptable nature. We argue that truly understanding values in LLMs requires considering both "know what" and "know why". To this end, we present the Value Understanding Measurement (VUM) framework that quantitatively assess both "know what" and "know why" by measuring the discriminator-critique gap related to human values. Using the Schwartz Value Survey, we specify our evaluation values and develop a thousand-level dialogue dataset with GPT-4. Our assessment looks at both the value alignment of LLM's outputs compared to baseline answers and how LLM responses align with reasons for value recognition versus GPT-4's annotations. We evaluate five representative LLMs and provide strong evidence that the scaling law significantly impacts "know what" but not much on "know why", which has consistently maintained a high level. This may further suggest that LLMs might craft plausible explanations based on the provided context without truly understanding their inherent value, indicating potential risks.

通过使用价值理解测量框架（VUM） quantitatively评估“知道什么”和“知道为什么”，我们评估了五个典型的大型语言模型。结果显示，扩展法则显著影响“知道什么”，但对“知道为什么”的影响不大，而后者始终保持在较高的水平。这可能进一步表明，大型语言模型可能会根据提供的上下文构建合理的解释，但并不真正理解其中的内在价值，表明潜在的风险。

通过判别-评论间隙测量语言模型中的价值理解