TopK-Guided: Adaptive, Budget-Aware Activation Sparsity for Efficient LLM Inference
cs.AI
Submitted: 2026-10-01
Updated: 2026-10-01
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- SliceGPT: Compress Large Language Models by Deleting Rows and Columns
- QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs
- WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference
- BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
- The Llama 3 Herd of Models
- Measuring Massive Multitask Language Understanding
- The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers
- Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time
- ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models
- The LAMBADA dataset: Word prediction requiring a broad discourse context
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- A Simple and Effective Pruning Approach for Large Language Models
- Llama 2: Open Foundation and Fine-Tuned Chat Models
- Crowdsourcing Multiple Choice Science Questions
- Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity
- HellaSwag: Can a Machine Really Finish Your Sentence?
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