COLA：通过适配器的自编码器检索实现持续学习
COLA: Continual Learning via Autoencoder Retrieval of Adapters
AI总结：
本文提出COLA框架，利用自编码器检索任务适配器嵌入，实现LLM高效持续学习，防止灾难性遗忘，无需数据回放，显著减少参数与内存，并在多数据集上超越现有方法。
摘要：
随时间学习一系列任务，也称为持续学习（CL），由于灾难性遗忘，是人工智能中最具挑战性的问题之一。大型语言模型（LLM）通常因训练所需的高计算资源成本，而不适合频繁重新训练和持续学习。此外，LLM不适合持续学习，因为随时间更新这些模型以获取新知识会导致覆盖现有知识，从而引发通常被称为“灾难性遗忘”的常见现象。在本文中，我们旨在通过一种新颖的框架COLA来解决这些问题，该框架使用自编码器来学习捕获与各种任务相关的权重的低维嵌入。我们的方法促进了知识向新任务的迁移，同时防止灾难性遗忘，且无需使用数据回放或大量特定于任务的参数。我们的方法COLA使LLM能够以最小的训练高效学习新任务，在先前任务上性能下降微不足道，并消除了保留早期训练数据的需要。在不同数据集（从面向任务的对话系统到意图分类数据集）上的实证评估表明，我们的方法不仅克服了灾难性遗忘，而且在多个任务中实现了参数使用和内存大小的显著减少，并在多个数据集上优于现有的最先进方法。
英文摘要：
Learning a set of tasks over time, also known as continual learning (CL), is one of the most challenging problems in artificial intelligence due to catastrophic forgetting. Large language models (LLMs) are often impractical to frequent re-training and continual learning , due to high cost of computational resources for training. Moreover, LLM are not suitable for continual learning as updating these models over time for acquiring new knowledge leads to overwrites existing knowledge leading to common phenomenon know as \textit{catastrophic forgetting}. In this paper, we aim to address these concerns using a novel framework , COLA that employs an autoencoder to learn capture low-dimensional embeddings of the weights associated with various tasks. Our approach facilitates the transfer of knowledge to new tasks while preventing catastrophic forgetting, all without using data replay or a substantial set of task-specific parameters. Our approach, COLA, makes the LLM efficiently learn new tasks with minimal training, insignificant performance degradation on previous tasks, and eliminates the need for retaining earlier training data. Empirical evaluation on different datasets ranging from task oriented dialouge system to intent classsfication datasets showcases that our method not only overcomes catastrophic forgetting but also achieves significant reduction in parameter usage and memory size, across multiple tasks and outperforming the existing state of the art methods across multiple datasets.
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。
