Summary of Polynomial Regression As a Task For Understanding In-context Learning Through Finetuning and Alignment, by Max Wilcoxson et al.
Polynomial Regression as a Task for Understanding In-context Learning Through Finetuning and Alignment
by Max Wilcoxson, Morten Svendgård, Ria Doshi, Dylan Davis, Reya Vir, Anant Sahai
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper proposes using univariate polynomial regression as a simple function class to better understand transformer-based architectures’ in-context-learning capabilities. This approach allows for the exploration of prompting and alignment within models, which was previously lacking in existing toy problems like linear regression or multi-layer-perceptrons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists use a new kind of math problem to help them understand how big language models learn from context. They want to see what happens when they give these models special instructions and check if the answers are correct. To do this, they need a simple way to test their ideas, so they created a type of math problem that can be used to explore this concept. |
Keywords
» Artificial intelligence » Alignment » Linear regression » Prompting » Regression » Transformer
