[论文解读] Towards Incremental Learning in Large Language Models: A Critical Review

Mladjan Jovanovic, Peter Voß|arXiv (Cornell University)|Apr 28, 2024

Topic Modeling被引用 8

一句话总结

本论文综述大语言模型中的增量学习，强调持续学习、元学习、参数高效学习和专家混合学习等范式，并指出一个差距：大多数方法并不更新核心模型，且没有任何方法能实时增量运行。

ABSTRACT

Incremental learning is the ability of systems to acquire knowledge over time, enabling their adaptation and generalization to novel tasks. It is a critical ability for intelligent, real-world systems, especially when data changes frequently or is limited. This review provides a comprehensive analysis of incremental learning in Large Language Models. It synthesizes the state-of-the-art incremental learning paradigms, including continual learning, meta-learning, parameter-efficient learning, and mixture-of-experts learning. We demonstrate their utility for incremental learning by describing specific achievements from these related topics and their critical factors. An important finding is that many of these approaches do not update the core model, and none of them update incrementally in real-time. The paper highlights current problems and challenges for future research in the field. By consolidating the latest relevant research developments, this review offers a comprehensive understanding of incremental learning and its implications for designing and developing LLM-based learning systems.

研究动机与目标

• 定义对大语言模型的增量学习，并阐明其在应对数据不断变化或数据有限时的重要性。
• 综合前沿的增量学习范式及其在大语言模型中的适用性。
• 识别当前方法的关键因素和局限性，为未来研究提供方向。

提出的方法

• 对持续学习、元学习、参数高效学习和专家混合学习等领域的文献进行调研与综合。
• 绘制各范式如何支持大语言模型的增量知识获取。
• 强调在增量环境中影响成功的实际考虑因素和要素。

实验结果

研究问题

• RQ1 哪些增量学习范式与大语言模型最相关？
• RQ2 在更新模型和实时适应方面，这些范式的表现如何？
• RQ3 阻碍大语言模型真正增量学习的关键挑战和差距是什么？

主要发现

• 许多增量学习方法并不更新核心模型。
• 在调研的方法中，没有任何方法实现对大语言模型的真正实时增量更新。
• 本综述汇总了近期研究，提出大语言模型增量学习的挑战与未来方向。