Summary of Generation Through the Lens Of Learning Theory, by Jiaxun Li et al.
Generation through the lens of learning theory
by Jiaxun Li, Vinod Raman, Ambuj Tewari
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 explores generation through the lens of statistical learning theory, building on the works of Gold, Angluin, and Kleinberg & Mullainathan. The authors formalize previous results in terms of a binary hypothesis class over an abstract example space. They then introduce two new settings: uniform and non-uniform generation, and provide a characterization of which hypothesis classes are generatable in these settings. The characterizations rely on the finiteness of a combinatorial dimension called the Closure dimension. By comparing generatability with predictability (captured via PAC and online learnability), the authors show that these two properties are incompatible – some classes are generatable but not predictable, while others are predictable but not generatable. The paper also extends its results to capture prompted generation, providing a complete characterization of which classes are prompt generatable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machines can learn and create new things. It takes ideas from earlier scientists like Gold and Angluin and uses them in a special way to understand what makes something “generable” or able to be created by a machine. The authors also explore two new ways that machines can generate things: uniformly and non-uniformly. They show that these ways of generating things are different from how well machines can predict what will happen next, and that some things can be generated but not predicted, and vice versa. The paper also talks about “prompted generation,” which is when a machine creates something in response to a specific prompt or instruction. |
Keywords
» Artificial intelligence » Prompt
