[2603.04678] Optimizing Language Models for Crosslingual Knowledge Consistency

Computer Science > Computation and Language arXiv:2603.04678 (cs) [Submitted on 4 Mar 2026] Title:Optimizing Language Models for Crosslingual Knowledge Consistency Authors:Tianyu Liu, Jirui Qi, Mrinmaya Sachan, Ryan Cotterell, Raquel Fernández, Arianna Bisazza View a PDF of the paper titled Optimizing Language Models for Crosslingual Knowledge Consistency, by Tianyu Liu and 5 other authors View PDF HTML (experimental) Abstract:Large language models are known to often exhibit inconsistent knowledge. This is particularly problematic in multilingual scenarios, where models are likely to be asked similar questions in different languages, and inconsistent responses can undermine their reliability. In this work, we show that this issue can be mitigated using reinforcement learning with a structured reward function, which leads to an optimal policy with consistent crosslingual responses. We introduce Direct Consistency Optimization (DCO), a DPO-inspired method that requires no explicit reward model and is derived directly from the LLM itself. Comprehensive experiments show that DCO significantly improves crosslingual consistency across diverse LLMs and outperforms existing methods when training with samples of multiple languages, while complementing DPO when gold labels are available. Extra experiments demonstrate the effectiveness of DCO in bilingual settings, significant out-of-domain generalizability, and controllable alignment via direction hyperparameters. Taken together, th...