Semantic Specialization in MoE Appears with Scale: A Study of DeepSeek R1 Expert Specialization

• URL: http://arxiv.org/abs/2502.10928v1
• Date: Sat, 15 Feb 2025 23:37:32 GMT
• Title: Semantic Specialization in MoE Appears with Scale: A Study of DeepSeek R1 Expert Specialization
• Authors: Matthew Lyle Olson, Neale Ratzlaff, Musashi Hinck, Man Luo, Sungduk Yu, Chendi Xue, Vasudev Lal,
• Abstract summary: DeepSeek-R1, the largest open-source Mixture-of-Experts (MoE) model, has demonstrated reasoning capabilities comparable to proprietary frontier models. We investigate whether its routing mechanism exhibits greater semantic specialization than previous MoE models. We conclude that DeepSeek-R1's routing mechanism is more semantically aware and it engages in structured cognitive processes.
• Score: 7.457737671087695
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• Abstract: DeepSeek-R1, the largest open-source Mixture-of-Experts (MoE) model, has demonstrated reasoning capabilities comparable to proprietary frontier models. Prior research has explored expert routing in MoE models, but findings suggest that expert selection is often token-dependent rather than semantically driven. Given DeepSeek-R1's enhanced reasoning abilities, we investigate whether its routing mechanism exhibits greater semantic specialization than previous MoE models. To explore this, we conduct two key experiments: (1) a word sense disambiguation task, where we examine expert activation patterns for words with differing senses, and (2) a cognitive reasoning analysis, where we assess DeepSeek-R1's structured thought process in an interactive task setting of DiscoveryWorld. We conclude that DeepSeek-R1's routing mechanism is more semantically aware and it engages in structured cognitive processes.

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