Summary of In-context Learning and Occam’s Razor, by Eric Elmoznino et al.
In-context learning and Occam’s razor
by Eric Elmoznino, Tom Marty, Tejas Kasetty, Leo Gagnon, Sarthak Mittal, Mahan Fathi, Dhanya Sridhar, Guillaume Lajoie
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper explores the connection between Occam’s razor and in-context learning, a phenomenon where certain sequence models like Transformers learn from past observations to improve their performance. The authors show that the next-token prediction loss used to train these models is equivalent to a data compression technique called prequential coding, which jointly minimizes training error and model complexity. This research provides a normative account of in-context learning and highlights the shortcomings of current methods, suggesting ways for improvement. The authors use empirical experiments to support their theory and make their code available online. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how some machine learning models can get better at predicting things when they’re given more information from previous predictions. It’s like a puzzle where the model figures out what the next piece should be based on what came before. The researchers show that this process is connected to a mathematical idea called Occam’s razor, which says that simple explanations are usually the best ones. They also find that the way these models are trained is closely related to a technique used in data compression, where we try to shrink big files into smaller ones while still keeping all the important information. The authors’ work helps us understand how these models work and how they can be made even better. |
Keywords
» Artificial intelligence » Machine learning » Token
