Cross-lingual alignment, the meaningful similarity of representations across
languages in multilingual language models, has been an active field of research
in recent years. We survey the literature of techniques to improve
cross-lingual alignment, providing a taxonomy of methods and summarising
insights from throughout the field. We present different understandings of
cross-lingual alignment and their limitations. We provide a qualitative summary
of results from a large number of surveyed papers. Finally, we discuss how
these insights may be applied not only to encoder models, where this topic has
been heavily studied, but also to encoder-decoder or even decoder-only models,
and argue that an effective trade-off between language-neutral and
language-specific information is key.

近年来，跨语言对齐是多语言语言模型研究的一个活跃领域。本文概述了改进跨语言对齐的技术方法，并总结了该领域的见解。我们提供了对大量论文的定性总结结果。最后，我们讨论了这些见解不仅适用于已经深入研究这一主题的编码器模型，还适用于编码器 - 解码器甚至只有解码器的模型，并指出平衡语言中立和语言特定信息的有效权衡是关键。

跨语言对齐的理解 -- 综述

Understanding Cross-Lingual Alignment -- A Survey

Large language models (LLMs) exhibit superior performance on various natural
language tasks, but they are susceptible to issues stemming from outdated data
and domain-specific limitations. In order to address these challenges,
researchers have pursued two primary strategies, knowledge editing and
retrieval augmentation, to enhance LLMs by incorporating external information
from different aspects. Nevertheless, there is still a notable absence of a
comprehensive survey. In this paper, we propose a review to discuss the trends
in integration of knowledge and large language models, including taxonomy of
methods, benchmarks, and applications. In addition, we conduct an in-depth
analysis of different methods and point out potential research directions in
the future. We hope this survey offers the community quick access and a
comprehensive overview of this research area, with the intention of inspiring
future research endeavors.

通过综述知识融入大型语言模型的趋势、方法分类、基准和应用，本文概述了该研究领域的全貌，并指出了未来的研究方向。