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Continual Learning in Large Language Models: Methods, Challenges, and Opportunities

arXiv:2603.12658v1 Announce Type: new Abstract: Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating catastrophic forgetting-a critical limitation of the static pre-training paradigm inherent to modern LLMs. This survey presents a comprehensive overview of CL methodologies tailored for LLMs, structured around three core training stages: continual pre-training, continual fine-tuning, and continual alignment.Beyond the canonical taxonomy of rehearsal-, regularization-, and architecture-based methods, we further subdivide each category by its distinct forgetting mitigation mechanisms and conduct a rigorous comparative analysis of the adaptability and critical improvements of traditional CL methods for LLMs. In doing so, we explicitly highlight core distinctions between LLM CL and traditional machine learning, particularly with respect to scale, parameter efficiency

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Hongyang Chen, Zhongwu Sun, Hongfei Ye, Kunchi Li, Xuemin Lin · · 1 min read · 8 views
#cs.CL #cs.AI

arXiv:2603.12658v1 Announce Type: new Abstract: Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating catastrophic forgetting-a critical limitation of the static pre-training paradigm inherent to modern LLMs. This survey presents a comprehensive overview of CL methodologies tailored for LLMs, structured around three core training stages: continual pre-training, continual fine-tuning, and continual alignment.Beyond the canonical taxonomy of rehearsal-, regularization-, and architecture-based methods, we further subdivide each category by its distinct forgetting mitigation mechanisms and conduct a rigorous comparative analysis of the adaptability and critical improvements of traditional CL methods for LLMs. In doing so, we explicitly highlight core distinctions between LLM CL and traditional machine learning, particularly with respect to scale, parameter efficiency, and emergent capabilities. Our analysis covers essential evaluation metrics, including forgetting rates and knowledge transfer efficiency, along with emerging benchmarks for assessing CL performance. This survey reveals that while current methods demonstrate promising results in specific domains, fundamental challenges persist in achieving seamless knowledge integration across diverse tasks and temporal scales. This systematic review contributes to the growing body of knowledge on LLM adaptation, providing researchers and practitioners with a structured framework for understanding current achievements and future opportunities in lifelong learning for language models.

Executive Summary

This article presents a comprehensive survey of continual learning (CL) methodologies tailored for large language models (LLMs). The authors structure their analysis around three core training stages: continual pre-training, continual fine-tuning, and continual alignment. They subdivide each category by its distinct forgetting mitigation mechanisms and conduct a rigorous comparative analysis of traditional CL methods for LLMs. The authors highlight core distinctions between LLM CL and traditional machine learning, particularly with respect to scale, parameter efficiency, and emergent capabilities. The survey reveals promising results in specific domains but underscores fundamental challenges in achieving seamless knowledge integration across diverse tasks and temporal scales. This systematic review contributes to the growing body of knowledge on LLM adaptation, providing researchers and practitioners with a structured framework for understanding current achievements and future opportunities in lifelong learning for language models.

Key Points

  • ▸ The authors present a comprehensive survey of CL methodologies tailored for LLMs.
  • ▸ The analysis is structured around three core training stages: continual pre-training, continual fine-tuning, and continual alignment.
  • ▸ The authors conduct a rigorous comparative analysis of traditional CL methods for LLMs, highlighting core distinctions between LLM CL and traditional machine learning.

Merits

Comprehensive Analysis

The authors provide a thorough and systematic review of CL methodologies for LLMs, covering essential evaluation metrics and emerging benchmarks.

Structured Framework

The survey offers a structured framework for understanding current achievements and future opportunities in lifelong learning for language models.

Comparative Analysis

The authors conduct a rigorous comparative analysis of traditional CL methods for LLMs, highlighting core distinctions between LLM CL and traditional machine learning.

Demerits

Limited Scope

The survey focuses on CL methodologies tailored for LLMs and may not be applicable to other types of machine learning models.

Fundamental Challenges

The authors highlight fundamental challenges in achieving seamless knowledge integration across diverse tasks and temporal scales, which may limit the practical applications of CL for LLMs.

Expert Commentary

This article presents a timely and comprehensive survey of CL methodologies tailored for LLMs. The authors' structured analysis and comparative evaluation of traditional CL methods for LLMs provide valuable insights into the current state of the field and highlight future opportunities for research and development. However, the survey's limited scope and fundamental challenges in achieving seamless knowledge integration across diverse tasks and temporal scales may limit its practical applications. Nevertheless, the analysis and recommendations presented in this article will likely inform the development of more efficient and effective language models in the future.

Recommendations

  • ✓ Future research should focus on developing more effective CL methodologies for LLMs that can address fundamental challenges in achieving seamless knowledge integration across diverse tasks and temporal scales.
  • ✓ The development of more efficient and effective CL performance metrics and emerging benchmarks can guide the evaluation and comparison of LLMs.

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