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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.19034 |
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| _version_ | 1866912252927410176 |
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| author | Jelassi, Samy Mohri, Clara Brandfonbrener, David Gu, Alex Vyas, Nikhil Anand, Nikhil Alvarez-Melis, David Li, Yuanzhi Kakade, Sham M. Malach, Eran |
| author_facet | Jelassi, Samy Mohri, Clara Brandfonbrener, David Gu, Alex Vyas, Nikhil Anand, Nikhil Alvarez-Melis, David Li, Yuanzhi Kakade, Sham M. Malach, Eran |
| contents | The Mixture-of-Experts (MoE) architecture enables a significant increase in the total number of model parameters with minimal computational overhead. However, it is not clear what performance tradeoffs, if any, exist between MoEs and standard dense transformers. In this paper, we show that as we increase the number of experts (while fixing the number of active parameters), the memorization performance consistently increases while the reasoning capabilities saturate. We begin by analyzing the theoretical limitations of MoEs at reasoning. We prove that there exist graph problems that cannot be solved by any number of experts of a certain width; however, the same task can be easily solved by a dense model with a slightly larger width. On the other hand, we find that on memory-intensive tasks, MoEs can effectively leverage a small number of active parameters with a large number of experts to memorize the data. We empirically validate these findings on synthetic graph problems and memory-intensive closed book retrieval tasks. Lastly, we pre-train a series of MoEs and dense transformers and evaluate them on commonly used benchmarks in math and natural language. We find that increasing the number of experts helps solve knowledge-intensive tasks, but fails to yield the same benefits for reasoning tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19034 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mixture of Parrots: Experts improve memorization more than reasoning Jelassi, Samy Mohri, Clara Brandfonbrener, David Gu, Alex Vyas, Nikhil Anand, Nikhil Alvarez-Melis, David Li, Yuanzhi Kakade, Sham M. Malach, Eran Machine Learning The Mixture-of-Experts (MoE) architecture enables a significant increase in the total number of model parameters with minimal computational overhead. However, it is not clear what performance tradeoffs, if any, exist between MoEs and standard dense transformers. In this paper, we show that as we increase the number of experts (while fixing the number of active parameters), the memorization performance consistently increases while the reasoning capabilities saturate. We begin by analyzing the theoretical limitations of MoEs at reasoning. We prove that there exist graph problems that cannot be solved by any number of experts of a certain width; however, the same task can be easily solved by a dense model with a slightly larger width. On the other hand, we find that on memory-intensive tasks, MoEs can effectively leverage a small number of active parameters with a large number of experts to memorize the data. We empirically validate these findings on synthetic graph problems and memory-intensive closed book retrieval tasks. Lastly, we pre-train a series of MoEs and dense transformers and evaluate them on commonly used benchmarks in math and natural language. We find that increasing the number of experts helps solve knowledge-intensive tasks, but fails to yield the same benefits for reasoning tasks. |
| title | Mixture of Parrots: Experts improve memorization more than reasoning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.19034 |