Summary of U-shaped and Inverted-u Scaling Behind Emergent Abilities Of Large Language Models, by Tung-yu Wu and Pei-yu Lo
U-shaped and Inverted-U Scaling behind Emergent Abilities of Large Language Models
by Tung-Yu Wu, Pei-Yu Lo
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 explores the phenomenon of emergent abilities in large language models (LLMs) on downstream tasks. The authors investigate this by grouping questions based on difficulty level and provide a possible explanation for emergent abilities. They observe U-shaped scaling for hard questions and inverted-U scaling followed by steady improvement for easy questions, which initially offset each other, causing stagnant overall performance. As the scaling pattern of easy questions reverts from inverse to standard scaling, the performance starts to soar, leading to emergent abilities. The authors propose a simple yet effective pipeline, called Slice-and-Sandwich, to predict the emergence threshold and model performance beyond the threshold. This work has implications for improving LLMs’ abilities in various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how big language models get better at certain tasks as they get bigger. The scientists found that these models start out slow but then suddenly improve a lot when they reach a certain size. They think this happens because the models are initially getting worse at easy questions, which makes their overall performance look bad. But then, they start to get better at easy questions and this helps them get better overall. The researchers came up with a way to predict when these big language models will start getting better and how well they’ll do. |
