Outlier Dimension

Outlier dimensions, representing high-variance features in high-dimensional data, are a focus of current research across diverse fields. Studies examine their impact on model performance and robustness, particularly within large language models (LLMs) and multivariate statistical problems like state estimation. Current research investigates how these dimensions encode task-specific knowledge, influence model decisions, and relate to data characteristics such as token frequency in LLMs or residual distributions in state estimation. Understanding and mitigating the effects of outlier dimensions is crucial for improving model accuracy, efficiency, and robustness in various applications.