Summary of Towards Lifelong Learning Of Large Language Models: a Survey, by Junhao Zheng et al.
Towards Lifelong Learning of Large Language Models: A Survey
by Junhao Zheng, Shengjie Qiu, Chengming Shi, Qianli Ma
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new survey on lifelong learning for large language models (LLMs) is presented, focusing on adapting to dynamic data and tasks. Traditional training methods are insufficient for coping with ongoing changes, so this approach enables LLMs to learn continuously while retaining previously learned information. The survey categorizes strategies into Internal Knowledge (continual pretraining and finetuning) and External Knowledge (retrieval-based and tool-based lifelong learning). Key contributions include a novel taxonomy of 12 scenarios, identifying common techniques across all scenarios, and highlighting emerging techniques like model expansion and data selection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better at doing many things, but they can only do these things if they keep learning from new information. Right now, the way we train these models is not very good at keeping up with changes in what we want them to do or what kind of information is out there. This makes it hard for the models to learn and remember new things without forgetting old things. Lifelong learning is a way to fix this by letting the models keep learning and adapting as they go, so they can get better and better at doing their job. |
Keywords
» Artificial intelligence » Pretraining
