In our analysis of expert selection via the router, we measured the distribution of selected experts across various domains using The Pile validation dataset. Surprisingly, we found no clear domain-based patterns in expert assignment across different document types like ArXiv, PubMed, and PhilPapers. Only DM Mathematics displayed a marginally different assignment. This behavior indicates a structured syntactic routing by the model, although it is not entirely specialized to specific topics.
The analysis revealed that at different layers, the expert assignments appear consistently similar across various domains such as mathematics and biology. Interestingly, we noticed this consistency across the first and last layers of the model, which are closely correlated with the input and output embeddings. This may reflect the robustness of the router's design rather than a specific adaptation to each domain.
Through our examination of the expert routing process, we observed that certain tokens consistently partnered with particular experts, suggesting a structural consistency in expert engagement. For instance, in Python code, the token 'self' is frequently routed to the same expert as 'Question' in English documents. This implies some identifiable connection between specific terms across different domains, irrespective of their contextual usage.
Data from our routing analysis also indicated that certain syntactic structures — like indentation tokens in Python — were consistently assigned to the same experts, particularly noticeable in early and late layers. This reveals a significant level of systematic organization within the routing process, showcasing the model's ability to recognize and respond to specific syntactic cues beyond mere topical considerations.
#sparse-mixture-of-experts #expert-selection #routing-analysis #multilingual-performance #bias-benchmarks
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