Summary of Continual Learning For Large Language Models: a Survey, by Tongtong Wu et al.
Continual Learning for Large Language Models: A Survey
by Tongtong Wu, Linhao Luo, Yuan-Fang Li, Shirui Pan, Thuy-Trang Vu, Gholamreza Haffari
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the issue of frequent re-training for large language models (LLMs) due to their massive scale, which makes updates necessary to endow them with new skills and keep them up-to-date with rapidly evolving human knowledge. The authors survey recent works on continual learning for LLMs, cataloging techniques in a novel multi-staged categorization scheme that includes continual pretraining, instruction tuning, and alignment. They contrast these methods with simpler adaptation strategies used in smaller models and other enhancement approaches like retrieval-augmented generation and model editing. Additionally, the authors discuss benchmarks and evaluation methods, identifying challenges and future work directions for this crucial task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are super smart computers that can understand and generate human-like text. But they need to be updated often to learn new things and stay current with what humans know. This paper looks at how we can update LLMs without having to retrain them from scratch, which is expensive and time-consuming. The authors group different methods for updating LLMs into categories like “continual pretraining” and “instruction tuning.” They also compare these methods to simpler ways of adapting smaller models and other approaches that help improve language model performance. |
Keywords
* Artificial intelligence * Alignment * Continual learning * Instruction tuning * Language model * Pretraining * Retrieval augmented generation
