Out-of-distribution (OOD) detection plays a vital role in enhancing the
reliability of machine learning (ML) models. The emergence of large language
models (LLMs) has catalyzed a paradigm shift within the ML community,
showcasing their exceptional capabilities across diverse natural language
processing tasks. While existing research has probed OOD detection with smaller
encoder-based Transformers like BERT and RoBERTa, the stark differences in
scales, pre-training objectives, and inference paradigms call into question the
applicability of these findings to LLMs. This paper embarks on a pioneering
empirical investigation of OOD detection in the domain of LLMs, focusing on
LLaMA series ranging from 7B to 65B in size. We thoroughly evaluate
commonly-used OOD detectors, scrutinizing their performance in both zero-grad
and fine-tuning scenarios. Notably, we alter previous discriminative
in-distribution fine-tuning into generative fine-tuning, aligning the
pre-training objective of LLMs with downstream tasks. Our findings unveil that
a simple cosine distance OOD detector demonstrates superior efficacy,
outperforming other OOD detectors. We provide an intriguing explanation for
this phenomenon by highlighting the isotropic nature of the embedding spaces of
LLMs, which distinctly contrasts with the anisotropic property observed in
smaller BERT family models. The new insight enhances our understanding of how
LLMs detect OOD data, thereby enhancing their adaptability and reliability in
dynamic environments.

通过对大型语言模型进行实证研究，本文发现余弦距离的异常检测器表现出卓越的效力，优于其他异常检测器，并通过强调大型语言模型嵌入空间的各向同性特征，提供了对这一现象的有趣解释，进一步增强了我们对大型语言模型在检测异常数据方面的适应性和可靠性。