# Continual Learning for Large Language Models: A Survey

**Authors:** Tongtong Wu, Linhao Luo, Yuan-Fang Li, Shirui Pan, Thuy-Trang Vu, Gholamreza Haffari  
**arXiv:** [2402.01364](https://arxiv.org/abs/2402.01364)  
**Date:** 2024-02-02  
**Summary page:** https://www.summarizepaper.com/en/paper/2402.01364v1/

## Key points

- Large language models (LLMs) are essential tools in natural language processing for generating human-like text.
- LLMs are not easily re-trainable due to high costs associated with their massive scale.
- Continual learning techniques have been developed to update LLMs with new skills and align them with evolving human knowledge.
- The paper by Tongtong Wu et al. provides a survey of recent works on continual learning for LLMs, introducing a multi-staged categorization scheme for these techniques.
- The categorization scheme includes continual pretraining, instruction tuning, and alignment methods to help LLMs adapt and improve over time without complete re-training.
- Challenges faced in continually updating large language models are highlighted, comparing techniques with simpler adaptation methods used in smaller models and other enhancement strategies like retrieval-augmented generation and model editing.
- Benchmarks and evaluation metrics are discussed for assessing the effectiveness of continual learning techniques for LLMs.
- Key challenges that need to be addressed in future research efforts are identified to advance this crucial task.

## Summary

In the field of natural language processing, large language models (LLMs) have become essential tools for various tasks due to their ability to generate human-like text. However, these models are not easily re-trainable due to the high costs associated with their massive scale. To address this challenge, continual learning techniques have been developed to update LLMs with new skills and keep them aligned with evolving human knowledge. This paper by Tongtong Wu, Linhao Luo, Yuan-Fang Li, Shirui Pan, Thuy-Trang Vu, and Gholamreza Haffari provides a comprehensive survey of recent works on continual learning for LLMs. The authors introduce a novel multi-staged categorization scheme for continual learning techniques tailored specifically for LLMs. This scheme includes continual pretraining, instruction tuning, and alignment methods to ensure that LLMs can adapt and improve over time without the need for complete re-training. By comparing these techniques with simpler adaptation methods used in smaller models and other enhancement strategies like retrieval-augmented generation and model editing, the authors highlight the unique challenges faced in continually updating large language models. Furthermore, the paper discusses benchmarks and evaluation metrics used to assess the effectiveness of continual learning techniques for LLMs. Through this analysis, the authors identify several key challenges that need to be addressed in future research efforts in order to further advance this crucial task. Overall, this survey provides valuable insights into the state-of-the-art approaches for continually improving large language models and sets a foundation for future developments in this rapidly evolving field of study.