通过实体对齐提升多语言模型的事实一致性。
On the Entity-Level Alignment in Crosslingual Consistency
- 基于主客体翻译任务检验跨语言实体对齐
- 实体错位导致事实不一致，对齐度与一致性强相关
- 新方法在多语言提示中注入英文实体名，显著提升准确率
多语言大模型应能在不同语言间保持事实知识的一致性，但其一致性的成因及频繁失败的原因仍不明确。本文假设这些不一致源于实体对齐失败，即主语和宾语在跨语言概念空间中的映射失效。通过实体级（主语与宾语）翻译任务评估对齐情况，发现所有测试模型中，一致性与对齐度高度相关，主语或宾语的错位常导致不一致。基于此，提出SubSub与SubInj两种方法，在跨语言提示中融入英语主语翻译，显著提升事实回忆准确率与一致性。机制分析表明，这些干预通过模型内部的枢纽语言处理强化了概念空间中的实体表示对齐，为多语言事实预测提供了有效且实用的策略。
原文摘要 · Abstract (English)
Multilingual large language models (LLMs) are expected to recall factual knowledge consistently across languages. However, the factors that give rise to such crosslingual consistency -- and its frequent failure -- remain poorly understood. In this work, we hypothesize that these inconsistencies may arise from failures in entity alignment, the process of mapping subject and object entities into a shared conceptual space across languages. To test this, we assess alignment through entity-level (subject and object) translation tasks, and find that consistency is strongly correlated with alignment across all studied models, with misalignment of subjects or objects frequently resulting in inconsistencies. Building on this insight, we propose SubSub and SubInj, two effective methods that integrate English translations of subjects into prompts across languages, leading to substantial gains in both factual recall accuracy and consistency. Finally, our mechanistic analysis reveals that these interventions reinforce the entity representation alignment in the conceptual space through model's internal pivot-language processing, offering effective and practical strategies for improving multilingual factual prediction.
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