Can We Trust LLM Detectors? (arxiv.org)

Recent research has highlighted significant weaknesses in current large language model (LLM) detectors, particularly in real-world applications. Existing detection methods, both training-free and supervised, struggle to maintain performance under distribution shifts, with their reliability plummeting when encountering novel text generators or simple style changes. The study reveals that while supervised detectors perform well within familiar contexts, they falter outside their training domains. This inconsistency raises concerns about the overall trustworthiness of these detectors when applied to varied text sources. To tackle these challenges, the researchers propose a supervised contrastive learning (SCL) framework aimed at developing discriminative style embeddings. Preliminary findings suggest that this new approach could enhance the robustness of detection systems, making them more adaptable to diverse and evolving text presents. By exposing the limitations of current methodologies and introducing innovative solutions, this study is poised to spark further developments in creating reliable, domain-agnostic LLM detectors, critical for addressing the growing demand for effective AI text detection in varied applications.