  The emergent capabilities of Large Language Models (LLMs) have made it
crucial to align their values with those of humans. However, current
methodologies typically attempt to assign value as an attribute to LLMs, yet
lack attention to the ability to pursue value and the importance of
transferring heterogeneous values in specific practical applications. In this
paper, we propose a Heterogeneous Value Alignment Evaluation (HVAE) system,
designed to assess the success of aligning LLMs with heterogeneous values.
Specifically, our approach first brings the Social Value Orientation (SVO)
framework from social psychology, which corresponds to how much weight a person
attaches to the welfare of others in relation to their own. We then assign the
LLMs with different social values and measure whether their behaviors align
with the inducing values. We conduct evaluations with new auto-metric
\textit{value rationality} to represent the ability of LLMs to align with
specific values. Evaluating the value rationality of five mainstream LLMs, we
discern a propensity in LLMs towards neutral values over pronounced personal
values. By examining the behavior of these LLMs, we contribute to a deeper
insight into the value alignment of LLMs within a heterogeneous value system.
