  The rapid advancement of Large Language Models (LLMs) has attracted much
attention to value alignment for their responsible development. However, how to
define values in this context remains a largely unexplored question. Existing
work mainly follows the Helpful, Honest, Harmless principle and specifies
values as risk criteria formulated in the AI community, e.g., fairness and
privacy protection, suffering from poor clarity, adaptability and transparency.
Inspired by basic values in humanity and social science across cultures, this
work proposes a novel basic value alignment paradigm and introduces a value
space spanned by basic value dimensions. All LLMs' behaviors can be mapped into
the space by identifying the underlying values, possessing the potential to
address the three challenges. To foster future research, we apply the
representative Schwartz's Theory of Basic Values as an initialized example and
construct FULCRA, a dataset consisting of 5k (LLM output, value vector) pairs.
Our extensive analysis of FULCRA reveals the underlying relation between basic
values and LLMs' behaviors, demonstrating that our approach not only covers
existing mainstream risks but also anticipates possibly unidentified ones.
Additionally, we present an initial implementation of the basic value
evaluation and alignment, paving the way for future research in this line.
