Summary of Towards Incremental Learning in Large Language Models: a Critical Review, by Mladjan Jovanovic and Peter Voss
Towards Incremental Learning in Large Language Models: A Critical Review
by Mladjan Jovanovic, Peter Voss
First submitted to arxiv on: 28 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This review provides an in-depth analysis of incremental learning in Large Language Models (LLMs), a crucial ability for intelligent systems to adapt and generalize to novel tasks. The paper synthesizes the state-of-the-art incremental learning paradigms, including continual learning, meta-learning, parameter-efficient learning, and mixture-of-experts learning. Key findings include that many approaches do not update the core model and none update incrementally in real-time. The review highlights current problems and challenges for future research in the field, emphasizing the importance of designing and developing LLM-based learning systems capable of incremental learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how machines can learn new things over time, without forgetting what they already know. This ability is important for making machines smarter and able to do new tasks. The paper looks at different ways that machines have been taught to learn in this way, including meta-learning, continual learning, and parameter-efficient learning. It shows that while these methods work well, there are still some big challenges to overcome before we can make machines that can truly learn and adapt over time. |
Keywords
» Artificial intelligence » Continual learning » Meta learning » Mixture of experts » Parameter efficient
