  The remarkable achievements of Large Language Models (LLMs) have captivated
the attention of both academia and industry, transcending their initial role in
dialogue generation. The utilization of LLMs as intermediary agents in various
tasks has yielded promising results, sparking a wave of innovation in
artificial intelligence. Building on these breakthroughs, we introduce a novel
approach that integrates the agent paradigm into the Out-of-distribution (OOD)
detection task, aiming to enhance its robustness and adaptability. Our proposed
method, Concept Matching with Agent (CMA), employs neutral prompts as agents to
augment the CLIP-based OOD detection process. These agents function as dynamic
observers and communication hubs, interacting with both In-distribution (ID)
labels and data inputs to form vector triangle relationships. This triangular
framework offers a more nuanced approach than the traditional binary
relationship, allowing for better separation and identification of ID and OOD
inputs. Our extensive experimental results showcase the superior performance of
CMA over both zero-shot and training-required methods in a diverse array of
real-world scenarios.
