Summary of Continual Learning Using Only Large Language Model Prompting, by Jiabao Qiu et al.
Continual Learning Using Only Large Language Model Prompting
by Jiabao Qiu, Zixuan Ke, Bing Liu
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 The proposed CLOB paradigm revolutionizes continual learning (CL) by treating a large language model (LLM) as a black box, incrementally updating it through verbal prompts without fine-tuning or adding trainable parameters. This approach is particularly well-suited for LLMs accessible via APIs. The authors also introduce CIS, an incremental summarization technique that overcomes the LLM’s input length limit, achieving significant performance gains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CLOB is a new way to learn with large language models. Imagine you can teach an AI by giving it simple instructions, without changing its original settings. This approach is useful for models that can be accessed through special interfaces. The researchers also developed a method called CIS, which helps the model understand longer pieces of text and performs much better than previous methods. |
Keywords
» Artificial intelligence » Continual learning » Fine tuning » Large language model » Summarization
