  Prior research in representation engineering has revealed that LLMs encode
concepts within their representation spaces, predominantly centered around
English. In this study, we extend this philosophy to a multilingual scenario,
delving into multilingual human value concepts in LLMs. Through our
comprehensive exploration covering 7 types of human values, 16 languages and 3
LLM series with distinct multilinguality, we empirically substantiate the
existence of multilingual human values in LLMs. Further cross-lingual analysis
on these concepts discloses 3 traits arising from language resource
disparities: cross-lingual inconsistency, distorted linguistic relationships,
and unidirectional cross-lingual transfer between high- and low-resource
languages, all in terms of human value concepts. Additionally, we validate the
feasibility of cross-lingual control over value alignment capabilities of LLMs,
leveraging the dominant language as a source language. Drawing from our
findings on multilingual value alignment, we prudently provide suggestions on
the composition of multilingual data for LLMs pre-training: including a limited
number of dominant languages for cross-lingual alignment transfer while
avoiding their excessive prevalence, and keeping a balanced distribution of
non-dominant languages. We aspire that our findings would contribute to
enhancing the safety and utility of multilingual AI.
