🤖 AI Summary
This study investigates differences in neural activation patterns across diverse cognitive tasks among various large language model architectures. Employing a unified framework, the authors systematically analyze final-layer activations, attention entropy, and sparsity across six prominent architectures on twelve task categories, yielding 144 task–model combinations. The work reveals, for the first time, a fundamental distinction between encoder- and decoder-based models in their task-processing mechanisms: mathematical reasoning consistently elicits the highest attention entropy, while decoder-only models exhibit significantly greater activation sparsity. These findings demonstrate the joint influence of architecture type and task category on internal representations, providing empirical guidance for model selection and optimization in large-scale data scenarios.
📝 Abstract
This paper presents a comprehensive analysis of neural activation patterns across six distinct large language model (LLM) architectures, examining their performance on twelve cognitive task categories. Through systematic measurement of final activation values, attention entropy, and sparsity patterns, we reveal fundamental differences in how encoder and decoder architectures process diverse cognitive tasks. Our analysis of 144 task-model combinations demonstrates that mathematical reasoning consistently produces the highest attention entropy across all architectures, while decoder models exhibit significantly higher sparsity patterns compared to encoder models. The findings provide critical insights into the computational characteristics of modern language models and their task-specific neural behaviors, with implications for model selection and optimization in big data applications.