Towards Incremental Learning in Large Language Models: A Critical Review

2024-04-28

arXiv.org

AI Summary

Existing approaches to incremental learning for large language models (LLMs) suffer from high catastrophic forgetting, poor generalization, or inability to support true online adaptation—largely because they fail to enable real-time, progressive updates to LLMs’ core parameters. Method: We systematically survey four major paradigms—continual learning, meta-learning, parameter-efficient fine-tuning (e.g., LoRA, Adapters), and mixture-of-experts (MoE)—and identify, for the first time, that none achieve genuine real-time incremental updates to LLMs’ fundamental weight matrices. Contribution/Results: Based on this analysis, we propose the first comprehensive taxonomy for LLM incremental learning, rigorously delineating the capabilities and applicability boundaries of each paradigm. Furthermore, we articulate a forward-looking research direction that jointly prioritizes low catastrophic forgetting, strong cross-task knowledge generalization, and seamless online parameter updateability—establishing foundational principles for next-generation adaptive LLMs.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Natural Language Processing: (Large) Language Models
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Analyzing incremental learning paradigms in Large Language Models
• Addressing lack of real-time model updates in current approaches
• Identifying challenges for adaptive LLM systems in changing data environments

Innovation

• Combining continual meta-learning parameter-efficient mixture-of-experts paradigms
• Maintaining core model stability while acquiring new knowledge
• Enabling non-real-time incremental adaptation through specialized techniques