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[2509.00454] Universal Properties of Activation Sparsity in Modern Large Language Models
Summary
This article explores the universal properties of activation sparsity in modern large language models (LLMs), highlighting its implications for model efficiency and interpretability.
Why It Matters
Understanding activation sparsity is crucial for optimizing the performance of large language models, as it can enhance their efficiency and robustness. This research provides a comprehensive framework for evaluating sparsity in LLMs, addressing a significant gap in existing methodologies.
Key Takeaways
- Activation sparsity is essential for improving efficiency in LLMs.
- The potential for effective activation sparsity increases with model size.
- A general framework for evaluating sparsity in LLMs is introduced.
- The study provides insights into sparsity in diffusion-based LLMs.
- Practical guidance for leveraging activation sparsity in LLM design is offered.
Computer Science > Machine Learning arXiv:2509.00454 (cs) [Submitted on 30 Aug 2025 (v1), last revised 18 Feb 2026 (this version, v2)] Title:Universal Properties of Activation Sparsity in Modern Large Language Models Authors:Filip Szatkowski, Patryk Będkowski, Alessio Devoto, Jan Dubiński, Pasquale Minervini, Mikołaj Piórczyński, Simone Scardapane, Bartosz Wójcik View a PDF of the paper titled Universal Properties of Activation Sparsity in Modern Large Language Models, by Filip Szatkowski and 7 other authors View PDF HTML (experimental) Abstract:Activation sparsity is an intriguing property of deep neural networks that has been extensively studied in ReLU-based models, due to its advantages for efficiency, robustness, and interpretability. However, methods relying on exact zero activations do not directly apply to modern Large Language Models (LLMs), leading to fragmented, model-specific strategies for LLM activation sparsity and a gap in its general understanding. In this work, we introduce a general framework for evaluating sparsity robustness in contemporary LLMs and conduct a systematic investigation of this phenomenon in their feedforward~(FFN) layers. Our results uncover universal properties of activation sparsity across diverse model families and scales. Importantly, we observe that the potential for effective activation sparsity grows with model size, highlighting its increasing relevance as models scale. Furthermore, we present the first study of activation sparsi...