AVSS: Layer Importance Evaluation in Large Language Models via Activation Variance-Sparsity Analysis
This provides a method for optimizing LLM architectures by identifying non-essential layers, which is incremental but useful for efficiency in model deployment.
The paper tackled the problem of evaluating layer importance in large language models (LLMs) by proposing the Activation Variance-Sparsity Score (AVSS) metric, and found that removing the lowest 25% of layers based on AVSS retains over 90% of original performance across tasks like question answering and sentiment classification.
The evaluation of layer importance in deep learning has been an active area of research, with significant implications for model optimization and interpretability. Recently, large language models (LLMs) have gained prominence across various domains, yet limited studies have explored the functional importance and performance contributions of individual layers within LLMs, especially from the perspective of activation distribution. In this work, we propose the Activation Variance-Sparsity Score (AVSS), a novel metric combining normalized activation variance and sparsity to assess each layer's contribution to model performance. By identifying and removing approximately the lowest 25% of layers based on AVSS, we achieve over 90% of original model performance across tasks such as question answering, language modeling, and sentiment classification, indicating that these layers may be non-essential. Our approach provides a systematic method for identifying less critical layers, contributing to efficient large language model architectures.