Summary of Large Continual Instruction Assistant, by Jingyang Qiao and Zhizhong Zhang and Xin Tan and Yanyun Qu and Shouhong Ding and Yuan Xie
Large Continual Instruction Assistant
by Jingyang Qiao, Zhizhong Zhang, Xin Tan, Yanyun Qu, Shouhong Ding, Yuan Xie
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 introduces a framework for Continual Instruction Tuning (CIT) that allows Large Models to learn and adapt to new data while retaining knowledge from previous datasets. The authors observe that existing gradient updates can negatively impact performance on earlier datasets, and instead propose using Exponential Moving Average (EMA) to reduce forgetting. However, they note that EMA’s stable balance weight fails to address the ever-changing nature of datasets, leading to imbalances between plasticity and stability. To overcome this challenge, the authors develop a general framework for CIT that incorporates a trade-off between plasticity and stability, as well as an optimal balance weight determined by gradients and learned parameters. They also propose a stable-plasticity balanced coefficient to avoid knowledge confusion and allocate suitable training parameters for testing instances based on semantic similarity of instructions. The approach is demonstrated across multiple benchmarks, showcasing enhanced anti-forgetting capabilities and improved overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps Large Models learn from new data without forgetting what they already know. It’s like trying to remember a long list of things – you don’t want to forget the important stuff! The authors found that when they tried to update the model’s parameters, it often forgot what it had learned earlier. To solve this problem, they developed a special way to update the model using something called Exponential Moving Average (EMA). EMA helps the model remember what it learned before by averaging its past performance with its new learning. The authors also came up with a clever trick to balance how much the model adapts to new data versus remembering old knowledge. This approach worked really well and was tested on several different types of data, showing that it can help models learn more efficiently without forgetting what they already know. |
Keywords
» Artificial intelligence » Instruction tuning
