CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering
Yumeng Wang, Zhiyuan Fan, Qingyun Wang, Yi R. Fung, Heng Ji · 2025
Large Language Models (LLMs) are pretrained on extensive multilingual corpora to acquire both language-specific cultural knowledge and general knowledge.Ideally, while LLMs should provide consistent responses to cultureindependent questions across languages, we observe significant performance disparities.To address this, we explore the Cross-Lingual Self-Aligning ability of Language Models (CALM) to align knowledge across languages.Specifically, for a given question, we sample multiple responses across different languages, and select the most self-consistent response as the target, leaving the remaining responses as negative examples.We then employ direct preference optimization (DPO) to align the model's knowledge across different languages.Evaluations on the MEDQA and X-CSQA datasets demonstrate CALM's effectiveness in enhancing cross-lingual knowledge question answering, both in zero-shot and retrieval-augmented settings.We also found that increasing the number of languages involved in CALM training leads to higher accuracy and consistency.We offer a qualitative analysis of how cross-lingual consistency can enhance knowledge alignment and explore the method's generalizability 1 .