  Out-of-distribution (OOD) detection is crucial for model reliability, as it
identifies samples from unknown classes and reduces errors due to unexpected
inputs. Vision-Language Models (VLMs) such as CLIP are emerging as powerful
tools for OOD detection by integrating multi-modal information. However, the
practical application of such systems is challenged by manual prompt
engineering, which demands domain expertise and is sensitive to linguistic
nuances. In this paper, we introduce Label-driven Automated Prompt Tuning
(LAPT), a novel approach to OOD detection that reduces the need for manual
prompt engineering. We develop distribution-aware prompts with in-distribution
(ID) class names and negative labels mined automatically. Training samples
linked to these class labels are collected autonomously via image synthesis and
retrieval methods, allowing for prompt learning without manual effort. We
utilize a simple cross-entropy loss for prompt optimization, with cross-modal
and cross-distribution mixing strategies to reduce image noise and explore the
intermediate space between distributions, respectively. The LAPT framework
operates autonomously, requiring only ID class names as input and eliminating
the need for manual intervention. With extensive experiments, LAPT consistently
outperforms manually crafted prompts, setting a new standard for OOD detection.
Moreover, LAPT not only enhances the distinction between ID and OOD samples,
but also improves the ID classification accuracy and strengthens the
generalization robustness to covariate shifts, resulting in outstanding
performance in challenging full-spectrum OOD detection tasks. Codes are
available at \url{https://github.com/YBZh/LAPT}.
