重焕知识：通过结构化训练对灾难性干扰的预期性恢复
Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training
AI总结：
本文研究神经网络在循环呈现文档序列时的训练动态，发现大语言模型在顺序微调中表现出在再次遇到文档前主动恢复遗忘的预期性行为，并揭示了过参数化网络在循环结构化环境中克服灾难性干扰的新机制。
摘要：
我们探索了神经网络在结构化非独立同分布环境中的训练动态，在该环境中文档以固定、重复的顺序循环呈现。通常，网络在按文档序列训练时会遭受灾难性干扰；然而，我们发现按此方式顺序微调的大语言模型具有一个奇特且显著的特性：它们表现出预期性行为，在再次遇到文档之前就能从对文档的遗忘中恢复。即使这些文档从未在上下文中一起出现，这种行为也会发生。随着架构参数数量的增加，该行为会出现并变得更加稳健。通过全面的实验和可视化，我们展示了一种新机制，过参数化神经网络可以通过该机制从灾难性干扰中恢复，并揭示了在循环结构化环境中训练过参数化网络的新见解。
英文摘要：
We explore the training dynamics of neural networks in a structured non-IID setting where documents are presented cyclically in a fixed, repeated sequence. Typically, networks suffer from catastrophic interference when training on a sequence of documents; however, we discover a curious and remarkable property of LLMs finetuned sequentially in this setting: they exhibit anticipatory behavior, recovering from the forgetting on documents before encountering them again. This behavior occurs even though the documents are never presented in context together. The behavior emerges and becomes more robust as the architecture scales up its number of parameters. Through comprehensive experiments and visualizations, we demonstrate a new mechanism by which over-parametrized neural networks can recover from catastrophic interference and uncover new insights into training over-parameterized networks in cyclically structured environments.
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。
