Improving Cross-Lingual Factual Recall via Consistency-Driven Reinforcement Learning 事件

Improving Cross-Lingual Factual Recall via Consistency-Driven Reinforcement Learning arXiv:2606.06586v1 Announce Type: new Abstract: Large language models (LLMs) trained predominantly on English data encode substantial world knowledge, yet often fail to express it reliably in other languages, a phenomenon known as cross-lingual factual inconsistency. To study and address this, we introduce PolyFact, a large-scale parallel multilingual factual QA dataset containing 100K Wikidata-grounded facts a