Summary of Task Diversity Shortens the Icl Plateau, by Jaeyeon Kim et al.
Task Diversity Shortens the ICL Plateau
by Jaeyeon Kim, Sehyun Kwon, Joo Young Choi, Jongho Park, Jaewoong Cho, Jason D. Lee, Ernest K. Ryu
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 the phenomenon of long loss plateaus followed by rapid learning in simplified language models, known as in-context learning (ICL). Researchers have consistently observed this pattern, where models exhibit minimal improvement for extended periods before suddenly improving. The study reveals that training on multiple diverse ICL tasks simultaneously shortens these loss plateaus, making each task easier to learn. This finding contradicts the intuition that combined complexity would lengthen the learning process, instead suggesting that large-scale training of language models may be attributed not only to data richness but also to the easier optimization induced by natural language training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how language models learn from examples and new information. Researchers noticed that these models often learn slowly at first, then suddenly get much better. This study looks at why this happens when we train multiple language models on different tasks together. Surprisingly, it found that combining many tasks makes each task easier to learn! This is important because it might help explain how large language models are able to learn so well from the vast amount of text data available. |
Keywords
» Artificial intelligence » Optimization
