Researchers Propose Locality-Aware Redundancy Pruning to Improve LLM Inference Efficiency
Computer scientists have developed Locality-Aware Redundancy Pruning (LoRP), a new method for reducing the computational size of large language models by removing redundant layers without requiring retraining. The technique uses a Representation Locality Score to identify which layers can be safely pruned based on their similarity to other layers in the network. The approach could help make LLMs faster and more efficient to run, with potential applications across diverse AI systems.
Researchers have introduced LoRP, a training-free depth pruning framework designed to improve the inference efficiency of large language models by removing redundant network layers. The method is based on the observation that LLMs contain representational redundancy across their depth, but that this redundancy can be either localized to specific regions or distributed globally depending on the model architecture. LoRP uses a Representation Locality Score derived from inter-layer hidden-state similarity to characterize this phenomenon, then clusters similar layers and determines which ones can be pruned based on residual intra-cluster redundancy. Testing across multiple LLM families showed improvements in both perplexity metrics and downstream task performance. The approach requires only a small calibration set and no retraining, making it practical for deployment.
What different sources said
- arXiv cs.AICenter
LEAP: Learnable End-to-End Adaptive Pruning of Large Language Models
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