Representations from large language models (LLMs) are known to be dominated by a small subset of dimensions with exceedingly high variance. Previous works have argued that although ablating these outlier dimensions in LLM representations hurts downstream performance, outlier dimensions are detrimental to the representational quality of embeddings. In this study, we investigate how fine-tuning impacts outlier dimensions and show that 1) outlier dimensions that occur in pre-training persist in fine-tuned models and 2) a single outlier dimension can complete downstream tasks with a minimal error rate. Our results suggest that outlier dimensions can encode crucial task-specific knowledge and that the value of a representation in a single outlier dimension drives downstream model decisions.

翻译：大型语言模型（LLMs）的表示已知由一小部分方差极高的维度主导。先前的研究认为，虽然消除LLM表示中的这些异常维度会损害下游性能，但异常维度对嵌入的表示质量有害。在本研究中，我们探究了微调如何影响异常维度，并表明：1）预训练中出现的异常维度在微调模型中持续存在；2）单个异常维度能够以极低错误率完成下游任务。我们的结果表明，异常维度可以编码关键的任务特定知识，且表示在单个异常维度上的数值驱动着下游模型的决策。