A Systematic Evaluation of Cross-Lingual Consistency Enhancement Methods in Multilingual Language Models
Multilingual language models often produce inconsistent answers to semantically equivalent questions across languages, motivating methods to improve cross-lingual consistency (CLC). However, existing methods are typically evaluated using different models, tasks, and protocols, leaving their relative strengths unclear. In this work, we present a unified evaluation of representative CLC-enhancement methods for question answering, spanning inference-time interventions and post-training approaches across three model families and three closed-form benchmarks. The results show that post-training methods are generally more reliable, with direct distribution alignment consistently improving CLC a...
| Authors | Jirui Qi, Mingyang Wang, Hinrich Schütze et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 September 2026 |
| Last updated at source | 21 September 2026 |
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