Summary of Addressing the Abstraction and Reasoning Corpus Via Procedural Example Generation, by Michael Hodel
Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation
by Michael Hodel
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presented code enables procedural example generation for 400 ARC training tasks, using transformation logic similar to the original examples. Each task’s underlying distribution is reverse-engineered through sampling, aiming to cover as large a space of possible examples as reasonably possible. This approach lifts constraints like constant grid dimensions or symbol sets, allowing for diverse experiments and potentially leading to breakthroughs on the benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates code that can generate lots of examples for 400 training tasks in ARC. It does this by making a system that can sample from each task’s original examples’ underlying distribution. This helps create many more examples than just a few, which can be useful for trying out new ideas and potentially making big progress on the benchmark. |
