Summary of Mixture Of Parrots: Experts Improve Memorization More Than Reasoning, by Samy Jelassi et al.
Mixture of Parrots: Experts improve memorization more than reasoning
by Samy Jelassi, Clara Mohri, David Brandfonbrener, Alex Gu, Nikhil Vyas, Nikhil Anand, David Alvarez-Melis, Yuanzhi Li, Sham M. Kakade, Eran Malach
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed Mixture-of-Experts (MoE) architecture offers a scalable approach by allowing a significant increase in model parameters with minimal computational overhead. However, its performance tradeoffs with standard dense transformers remain unclear. This paper investigates the relationship between MoEs and dense transformers, showing that increasing the number of experts improves memorization capabilities while reasoning performance saturates. Theoretical analysis reveals limitations of MoEs for certain graph problems, but they excel in memory-intensive tasks by leveraging a small number of active parameters with many experts. Empirical validation is provided through synthetic graph problems and closed-book retrieval tasks. Pre-trained models are also evaluated on popular math and natural language benchmarks, revealing that increasing expert counts benefits knowledge-intensive tasks, but not reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoE architecture lets computers learn more things without using up too much computer power. But what happens when we use MoEs with transformers? This paper looks at how well they work together. They found that making MoEs have more “experts” helps them remember things better, but doesn’t make them better at solving problems. It’s like having a super smart student who can memorize lots of facts, but isn’t great at doing math problems. The researchers also tested these models on fun tasks like math and language puzzles and found that they’re good at some things, but not others. |
Keywords
* Artificial intelligence * Mixture of experts
