  Anomaly detection in computational workflows is critical for ensuring system
reliability and security. However, traditional rule-based methods struggle to
detect novel anomalies. This paper leverages large language models (LLMs) for
workflow anomaly detection by exploiting their ability to learn complex data
patterns. Two approaches are investigated: 1) supervised fine-tuning (SFT),
where pre-trained LLMs are fine-tuned on labeled data for sentence
classification to identify anomalies, and 2) in-context learning (ICL) where
prompts containing task descriptions and examples guide LLMs in few-shot
anomaly detection without fine-tuning. The paper evaluates the performance,
efficiency, generalization of SFT models, and explores zero-shot and few-shot
ICL prompts and interpretability enhancement via chain-of-thought prompting.
Experiments across multiple workflow datasets demonstrate the promising
potential of LLMs for effective anomaly detection in complex executions.
