In recent years, pre-trained Multilingual Language Models (MLLMs) have shown
a strong ability to transfer knowledge across different languages. However,
given that the aspiration for such an ability has not been explicitly
incorporated in the design of the majority of MLLMs, it is challenging to
obtain a unique and straightforward explanation for its emergence. In this
review paper, we survey literature that investigates different factors
contributing to the capacity of MLLMs to perform zero-shot cross-lingual
transfer and subsequently outline and discuss these factors in detail. To
enhance the structure of this review and to facilitate consolidation with
future studies, we identify five categories of such factors. In addition to
providing a summary of empirical evidence from past studies, we identify
consensuses among studies with consistent findings and resolve conflicts among
contradictory ones. Our work contextualizes and unifies existing research
streams which aim at explaining the cross-lingual potential of MLLMs. This
review provides, first, an aligned reference point for future research and,
second, guidance for a better-informed and more efficient way of leveraging the
cross-lingual capacity of MLLMs.

本综述论文旨在调查不同因素对多语言预训练语言模型实现零 - shot 跨语言转移的贡献，提供了对过去研究的实证证据概要，并识别一致性结果以及解决矛盾之处，进而为未来研究提供参考点与指导。

多语言语言模型跨语言转移因素的共同理解：一篇综述

Towards a Common Understanding of Contributing Factors for Cross-Lingual  Transfer in Multilingual Language Models: A Review

With an ever-increasing number of scientific papers published each year, it
becomes more difficult for researchers to explore a field that they are not
closely familiar with already. This greatly inhibits the potential for
cross-disciplinary research. A traditional introduction into an area may come
in the form of a review paper. However, not all areas and sub-areas have a
current review. In this paper, we present a method for the automatic generation
of a review paper corresponding to a user-defined query. This method consists
of two main parts. The first part identifies key papers in the area by their
bibliometric parameters, such as a graph of co-citations. The second stage uses
a BERT based architecture that we train on existing reviews for extractive
summarization of these key papers. We describe the general pipeline of our
method and some implementation details and present both automatic and expert
evaluations on the PubMed dataset.

本文介绍了一种方法，可以根据用户定义的查询自动生成一篇综述论文。该方法包括两个主要部分，第一部分通过诸如同袍引用的图表等文献计量参数来识别领域中的关键论文。第二阶段使用 BERT 架构，我们在现有的综述论文中对这些关键论文进行抽取式摘要。我们介绍了我们方法的一般流程和一些实现细节，并在 PubMed 数据集上进行了自动和专家评估。