The recent increase in data and model scale for language model pre-training
has led to huge training costs. In scenarios where new data become available
over time, updating a model instead of fully retraining it would therefore
provide significant gains. In this paper, we study the benefits and downsides
of updating a language model when new data comes from new languages - the case
of continual learning under language shift. Starting from a monolingual English
language model, we incrementally add data from Norwegian and Icelandic to
investigate how forward and backward transfer effects depend on the
pre-training order and characteristics of languages, for different model sizes
and learning rate schedulers. Our results show that, while forward transfer is
largely positive and independent of language order, backward transfer can be
either positive or negative depending on the order and characteristics of new
languages. To explain these patterns we explore several language similarity
metrics and find that syntactic similarity appears to have the best correlation
with our results.

对于语言模型的预训练，更新模型而不是完全重新训练，可以在新数据不断增加时提供显著的收益。本文研究了在语言转移的情况下，当新的数据来自新的语言时，更新语言模型的利与弊。通过将挪威语和冰岛语等数据逐步添加到单语英语语言模型中，我们研究了不同模型规模和学习率计划者的前向传递和后向传递的影响，并发现前向传递主要是积极的且与语言顺序无关，而后向传递则取决于新语言的顺序和特点而可能是积极的或消极的。为了解释这些模式，我们探索了几种语言相似性度量，并发现句法相似性与我们的结果有最好的相关性。

语言转变下的持续学习研究

A Study of Continual Learning Under Language Shift

The human brain can self-organize rich and diverse sparse neural pathways to
incrementally master hundreds of cognitive tasks. However, most existing
continual learning algorithms for deep artificial and spiking neural networks
are unable to adequately auto-regulate the limited resources in the network,
which leads to performance drop along with energy consumption rise as the
increase of tasks. In this paper, we propose a brain-inspired continual
learning algorithm with adaptive reorganization of neural pathways, which
employs Self-Organizing Regulation networks to reorganize the single and
limited Spiking Neural Network (SOR-SNN) into rich sparse neural pathways to
efficiently cope with incremental tasks. The proposed model demonstrates
consistent superiority in performance, energy consumption, and memory capacity
on diverse continual learning tasks ranging from child-like simple to complex
tasks, as well as on generalized CIFAR100 and ImageNet datasets. In particular,
the SOR-SNN model excels at learning more complex tasks as well as more tasks,
and is able to integrate the past learned knowledge with the information from
the current task, showing the backward transfer ability to facilitate the old
tasks. Meanwhile, the proposed model exhibits self-repairing ability to
irreversible damage and for pruned networks, could automatically allocate new
pathway from the retained network to recover memory for forgotten knowledge.

基于大脑启发的自适应神经路径重组的持续学习算法，在增量任务中通过自组织调控网络资源以有效应对多样化的认知任务，表现出卓越的性能、能量消耗和内存容量。具备学习更复杂任务、整合过去学习知识以及自我修复能力的特点。