  System logs play a critical role in maintaining the reliability of software
systems. Fruitful studies have explored automatic log-based anomaly detection
and achieved notable accuracy on benchmark datasets. However, when applied to
large-scale cloud systems, these solutions face limitations due to high
resource consumption and lack of adaptability to evolving logs. In this paper,
we present an accurate, lightweight, and adaptive log-based anomaly detection
framework, referred to as SeaLog. Our method introduces a Trie-based Detection
Agent (TDA) that employs a lightweight, dynamically-growing trie structure for
real-time anomaly detection. To enhance TDA's accuracy in response to evolving
log data, we enable it to receive feedback from experts. Interestingly, our
findings suggest that contemporary large language models, such as ChatGPT, can
provide feedback with a level of consistency comparable to human experts, which
can potentially reduce manual verification efforts. We extensively evaluate
SeaLog on two public datasets and an industrial dataset. The results show that
SeaLog outperforms all baseline methods in terms of effectiveness, runs 2X to
10X faster and only consumes 5% to 41% of the memory resource.
