跨语言探索参数化知识
Cross-Lingual Exploration for Parametric Knowledge
AI总结：
本文研究通过跨语言提示策略探索大型语言模型中的隐藏事实知识，发现跨语言探索能显著提升知识迁移和事实召回，比单语言扩展更高效，并改善跨语言一致性。
摘要：
大型语言模型中的参数化知识在不同语言中的可访问性并不相同。因此，标准推理技术往往难以呈现局部事实，导致跨语言知识迁移和一致性失败。在这项工作中，我们通过探索跨语言提示策略来研究访问隐藏事实知识的技术。我们确定了直接控制参数化知识检索的跨语言探索的四个固有维度，并在涵盖17种类型多样语言的 multilingual 事实基准上对其进行评估。我们的结果表明，跨语言探索显著改善了知识迁移和事实召回，代表了比母语扩展更高效的计算帕累托前沿。此外，我们观察到跨语言一致性的相应改进，这超出了仅凭准确率提升所能解释的范围。总体而言，我们的工作将多语言提示探索确立为一种高效的推理时策略，用于解锁潜在的参数化知识。
英文摘要：
Parametric knowledge in Large Language Models is not equally accessible across languages. As a result, standard inference techniques often struggle to surface localized facts, leading to failures in cross-lingual knowledge transfer and consistency. In this work, we investigate techniques for accessing hidden factual knowledge by exploring cross-lingual prompting strategies. We identify four inherent dimensions of cross-lingual exploration that directly govern parametric knowledge retrieval and evaluate them on multilingual factual benchmarks covering 17 typologically diverse languages. Our results demonstrate that cross-lingual exploration significantly improves knowledge transfer and factual recall, representing a more efficient compute Pareto frontier than native-language scaling. Furthermore, we observe corresponding improvements in cross-lingual consistency, exceeding what can be explained by accuracy gains alone. Overall, our work establishes multilingual prompt exploration as a highly effective inference-time strategy for unlocking latent parametric knowledge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
