Large language models (LLMs) have shown their potential in long-context
understanding and mathematical reasoning. In this paper, we study the problem
of using LLMs to detect tabular anomalies and show that pre-trained LLMs are
zero-shot batch-level anomaly detectors. That is, without extra
distribution-specific model fitting, they can discover hidden outliers in a
batch of data, demonstrating their ability to identify low-density data
regions. For LLMs that are not well aligned with anomaly detection and
frequently output factual errors, we apply simple yet effective data-generating
processes to simulate synthetic batch-level anomaly detection datasets and
propose an end-to-end fine-tuning strategy to bring out the potential of LLMs
in detecting real anomalies. Experiments on a large anomaly detection benchmark
(ODDS) showcase i) GPT-4 has on-par performance with the state-of-the-art
transductive learning-based anomaly detection methods and ii) the efficacy of
our synthetic dataset and fine-tuning strategy in aligning LLMs to this task.

利用大型语言模型（LLMs）来检测表格异常，并展示了预训练的 LLMs 在零样本批量级异常检测方面的效果，同时提出了一种简单但有效的数据生成过程和端到端微调策略来对齐 LLMs 检测真实异常的潜力。