LLM architectures revealed: math tasks fire neurons differently

A comprehensive study accepted at IEEE BigData 2025 has decoded how different large language model architectures handle cognitive tasks. Researchers Naser-Moghadasi and Ghaderi analyzed neural activation patterns—including final activation values, attention entropy, and sparsity—across six distinct LLM architectures. They tested performance on twelve cognitive task categories, creating 144 task-model combinations to systematically compare encoder and decoder designs.

The results are striking: mathematical reasoning consistently produced the highest attention entropy across all architectures, indicating intense focus on relationships between tokens. Decoder models, commonly used for generative tasks, exhibited significantly higher sparsity patterns compared to encoder models, meaning they activate fewer neurons for each prediction. These findings provide critical guidance for AI engineers: choose encoder architectures for tasks requiring dense attention (like classification), and decoders for generative scenarios where sparse activation improves efficiency. The study also highlights trade-offs in model optimization for big data applications, where understanding these neural behaviors can directly impact performance and cost.