Large Language Models (LLMs) serve as repositories of extensive world
knowledge, enabling them to perform tasks such as question-answering and
fact-checking. However, this knowledge can become obsolete as global contexts
change. In this paper, we introduce a novel problem in the realm of continual
learning: Online Continual Knowledge Learning (OCKL). This problem formulation
aims to manage the dynamic nature of world knowledge in LMs under real-time
constraints. We propose a new benchmark and evaluation metric designed to
measure both the rate of new knowledge acquisition and the retention of
previously learned knowledge. Our empirical evaluation, conducted using a
variety of state-of-the-art methods, establishes robust base-lines for OCKL.
Our results reveal that existing continual learning approaches are
unfortunately insufficient for tackling the unique challenges posed by OCKL. We
identify key factors that influence the trade-off between knowledge acquisition
and retention, thereby advancing our understanding of how to train LMs in a
continually evolving environment.

在这篇论文中，我们介绍了一个新的问题，在持续学习领域中称为 “在线持续知识学习（OCKL）”。我们提出了一个新的基准和评估指标，旨在测量新知识获取的速度和先前学到的知识的保留率。通过使用各种最先进的方法进行的实证评估为 OCKL 建立了稳健的基准。我们的结果显示，现有的持续学习方法对于应对 OCKL 提出的独特挑战不足够。我们确定了影响知识获取和保留之间权衡的关键因素，从而推进了我们对如何在不断演化的环境中训练 LLMs 的理解。

在线连续知识学习的语言模型

Online Continual Knowledge Learning for Language Models

Large language models (LLMs) encode a large amount of world knowledge.
However, as such knowledge is frozen at the time of model training, the models
become static and limited by the training data at that time. In order to
further improve the capacity of LLMs for knowledge-intensive tasks, we consider
augmenting LLMs with the large-scale web using search engine. Unlike previous
augmentation sources (e.g., Wikipedia data dump), the web provides broader,
more comprehensive and constantly updated information. In this paper, we
present a web-augmented LLM UNIWEB, which is trained over 16
knowledge-intensive tasks in a unified text-to-text format. Instead of simply
using the retrieved contents from web, our approach has made two major
improvements. Firstly, we propose an adaptive search engine assisted learning
method that can self-evaluate the confidence level of LLM's predictions, and
adaptively determine when to refer to the web for more data, which can avoid
useless or noisy augmentation from web. Secondly, we design a pretraining task,
i.e., continual knowledge learning, based on salient spans prediction, to
reduce the discrepancy between the encoded and retrieved knowledge. Experiments
on a wide range of knowledge-intensive tasks show that our model significantly
outperforms previous retrieval-augmented methods.

本文提出一种利用搜索引擎对大型语言模型进行网络增强的方法，通过自适应搜索引擎辅助学习和基于重要区间预测的连续知识学习任务，使模型在各种知识密集型任务中的性能表现显著优于基于检索的增强方法。

利用互联网优化大型语言模型

The Web Can Be Your Oyster for Improving Large Language Models

Large Language Models (LMs) are known to encode world knowledge in their
parameters as they pretrain on a vast amount of web corpus, which is often
utilized for performing knowledge-dependent downstream tasks such as question
answering, fact-checking, and open dialogue. In real-world scenarios, the world
knowledge stored in the LMs can quickly become outdated as the world changes,
but it is non-trivial to avoid catastrophic forgetting and reliably acquire new
knowledge while preserving invariant knowledge. To push the community towards
better maintenance of ever-changing LMs, we formulate a new continual learning
(CL) problem called Continual Knowledge Learning (CKL). We construct a new
benchmark and metric to quantify the retention of time-invariant world
knowledge, the update of outdated knowledge, and the acquisition of new
knowledge. We adopt applicable recent methods from literature to create several
strong baselines. Through extensive experiments, we find that CKL exhibits
unique challenges that are not addressed in previous CL setups, where parameter
expansion is necessary to reliably retain and learn knowledge simultaneously.
By highlighting the critical causes of knowledge forgetting, we show that CKL
is a challenging and important problem that helps us better understand and
train ever-changing LMs. The benchmark datasets, evaluation script, and
baseline code to reproduce our results are available at
this https URL

本文提出了一种新的连续学习问题 —— 连续知识学习，并构建了一个新的数据集和指标，用于衡量时间不变世界知识的保留，过时知识的更新和新知识的获取。通过实验发现，该问题的关键在于对抗知识遗忘和参数扩展，因此对于更好地维护不断变化的大型语言模型非常重要。