Exploring Multilingual Concepts of Human Value in Large Language Models: Is Value Alignment Consistent, Transferable and Controllable across Languages?
- URL: http://arxiv.org/abs/2402.18120v2
- Date: Tue, 16 Apr 2024 07:29:36 GMT
- Title: Exploring Multilingual Concepts of Human Value in Large Language Models: Is Value Alignment Consistent, Transferable and Controllable across Languages?
- Authors: Shaoyang Xu, Weilong Dong, Zishan Guo, Xinwei Wu, Deyi Xiong,
- Abstract summary: We explore 7 types of human values, 16 languages and 3 LLM series with distinct multilinguality.
Cross-lingual analysis on these concepts discloses 3 traits arising from language resource disparities.
We provide suggestions on the composition of multilingual data for LLMs pre-training.
- Score: 34.38469832305664
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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