Summary of Can Models Learn Skill Composition From Examples?, by Haoyu Zhao et al.
Can Models Learn Skill Composition from Examples?
by Haoyu Zhao, Simran Kaur, Dingli Yu, Anirudh Goyal, Sanjeev Arora
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper investigates compositional generalization in large language models (LLMs), a capability that enables them to combine learned skills in novel ways. The study introduces the SKILL-MIX evaluation, where LLMs are tasked with composing a paragraph using a specified number of language skills. While smaller models struggled to compose even with three skills, larger models like GPT-4 performed reasonably well with five or six skills. This research is relevant to AI safety and alignment, as compositional generalization can lead to more human-like intelligence. The authors’ approach provides valuable insights into the capabilities and limitations of LLMs in this domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to write a paragraph using words you’ve learned from reading books or having conversations. This is kind of like what computers are learning to do with language models. These models can combine different skills they’ve learned to create something new, which is important for making them smarter and more human-like. Researchers have come up with a way to test how well these models can do this by giving them tasks that require combining multiple skills. They found that smaller models struggled, but bigger models like GPT-4 did okay when asked to use five or six skills. This research is important for making sure AI systems are safe and work well. |
Keywords
» Artificial intelligence » Alignment » Generalization » Gpt
