Neural Activation Patterns Across Language Model Architectures: A Comprehensive Analysis of Cognitive Task Performance
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Computer Science > Computation and Language
Title:Neural Activation Patterns Across Language Model Architectures: A Comprehensive Analysis of Cognitive Task Performance
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.
| Comments: | 8 pages, accepted at IEEE BigData 2025 |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| ACM classes: | I.2.7; I.2.6 |
| Cite as: | arXiv:2605.15436 [cs.CL] |
| (or arXiv:2605.15436v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15436
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Mahdi Naser Moghadasi [view email][v1] Thu, 14 May 2026 21:31:19 UTC (12 KB)
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