Memory Augmented Language Models through Mixture of Word Experts (2024.naacl-long)
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| Challenge: | Increasing the parameter count of language models has been a primary driver of improved model quality, but increasing the model size also increases the cost of training and serving the model. |
| Approach: | They propose to decouple learning capacity and FLOPs by using a mixture-of-experts approach with large knowledge-rich vocabulary based routing functions. |
| Outcome: | The proposed model outperforms the T5 family of models with similar number of FLOPs on knowledge intensive tasks and similar performance to memory augmented approaches. |
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SimSMoE: Toward Efficient Training Mixture of Experts via Solving Representational Collapse (2025.findings-naacl)
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| Challenge: | Sparse mixture of experts (SMoE) is an effective approach for scaling large language models with a constant computational cost. |
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