Investigating Layer Importance in Large Language Models
This work addresses the opacity of LLMs, which hinders deployment in safety-critical scenarios and model improvement, by identifying key layers that dominate performance, though it is incremental in nature.
The study tackled the problem of understanding large language models (LLMs) by investigating the importance of individual layers, revealing that certain early 'cornerstone layers' are critical, with their removal drastically reducing performance to near-random guessing, while other layers cause only marginal changes.
Large language models (LLMs) have gained increasing attention due to their prominent ability to understand and process texts. Nevertheless, LLMs largely remain opaque. The lack of understanding of LLMs has obstructed the deployment in safety-critical scenarios and hindered the development of better models. In this study, we advance the understanding of LLM by investigating the significance of individual layers in LLMs. We propose an efficient sampling method to faithfully evaluate the importance of layers using Shapley values, a widely used explanation framework in feature attribution and data valuation. In addition, we conduct layer ablation experiments to assess the performance degradation resulting from the exclusion of specific layers. Our findings reveal the existence of cornerstone layers, wherein certain early layers can exhibit a dominant contribution over others. Removing one cornerstone layer leads to a drastic collapse of the model performance, often reducing it to random guessing. Conversely, removing non-cornerstone layers results in only marginal performance changes. This study identifies cornerstone layers in LLMs and underscores their critical role for future research.