Summary of The Broader Spectrum Of In-context Learning, by Andrew Kyle Lampinen et al.
The broader spectrum of in-context learning
by Andrew Kyle Lampinen, Stephanie C. Y. Chan, Aaditya K. Singh, Murray Shanahan
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 abstract explores the concept of supervised few-shot learning in language models, placing it within a broader framework of meta-learned in-context learning. The authors propose a unifying perspective that encompasses various abilities, including adapting to tasks from instructions or role-playing, and extrapolating time series. This perspective sheds light on potential roots of in-context learning in lower-level processing of linguistic dependencies. The authors highlight the importance of generalization, discussing its dimensions, such as flexibility in learning from different presentations and applying learned concepts. They also discuss connections to past literature in meta-learning and goal-conditioned agents, and other perspectives on learning and adaptation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models can learn a task quickly by understanding the context. This paper looks at how language models work when given only a few examples of a task. The authors suggest that this type of learning is connected to many other abilities, such as learning from instructions or understanding time series data. They also discuss why it’s important for language models to be able to generalize, which means they can apply what they’ve learned in different situations. |
Keywords
* Artificial intelligence * Few shot * Generalization * Meta learning * Supervised * Time series
