Summary of Meta-learning Adaptable Foundation Models, by Jacob L. Block et al.
Meta-Learning Adaptable Foundation Models
by Jacob L. Block, Sundararajan Srinivasan, Liam Collins, Aryan Mokhtari, Sanjay Shakkottai
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper introduces a novel approach to fine-tuning foundation models for downstream applications. By combining parameter-efficient fine-tuning (PEFT) with a meta-learning framework, the authors aim to learn adaptable representations that can be easily adapted to unseen tasks. The study focuses on linear models using low-rank adaptations and demonstrates the suboptimality of standard retraining for finding an adaptable set of parameters. Empirically, the proposed method is shown to outperform the conventional approach in a conversational prediction task using the ConvAI2 dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how we can make pre-trained models better at doing different tasks. We do this by fine-tuning the model, but first, we need to teach it to adapt to new situations. The authors came up with a clever way to do this using something called meta-learning. They tested their idea on a special kind of AI model and showed that it works really well. This is important because it means we can make AI models better at doing things like understanding what people are saying. |
Keywords
» Artificial intelligence » Fine tuning » Meta learning » Parameter efficient
