Summary of Simsmoe: Solving Representational Collapse Via Similarity Measure, by Giang Do et al.
SimSMoE: Solving Representational Collapse via Similarity Measure
by Giang Do, Hung Le, Truyen Tran
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach called Similarity-based Sparse Mixture of Experts (SimSMoE) addresses the representation collapse problem that hinders effective training of sparse mixture of experts (SMoE) models, which have gained popularity for scaling large language models while keeping computational costs constant. SimSMoE ensures a solution to this issue given a fixed FLOPs budget and outperforms other SMoE training methods in various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be trained more effectively with Similarity-based Sparse Mixture of Experts (SimSMoE). This new approach solves the representation collapse problem that makes it hard to train SMoE models. SimSMoE works within a fixed budget and does better than other training methods. |
Keywords
» Artificial intelligence » Mixture of experts
