Cascaded multi-granularity pruning reaches 13.8x compression on MHA+GELU LLMs for bearing fault diagnosis at 83.82% accuracy while causing ~74pp collapse on GQA+SwiGLU models that violate the formalized Structural Independence Assumption.
SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models
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Cascaded Multi-Granularity Pruning for On-Device LLM Inference in Industrial IoT
Cascaded multi-granularity pruning reaches 13.8x compression on MHA+GELU LLMs for bearing fault diagnosis at 83.82% accuracy while causing ~74pp collapse on GQA+SwiGLU models that violate the formalized Structural Independence Assumption.
- LLM Harms: A Taxonomy and Discussion