  The advent of Large Language Models (LLMs) has revolutionized text
generation, producing outputs that closely mimic human writing. This blurring
of lines between machine- and human-written text presents new challenges in
distinguishing one from the other a task further complicated by the frequent
updates and closed nature of leading proprietary LLMs. Traditional logits-based
detection methods leverage surrogate models for identifying LLM-generated
content when the exact logits are unavailable from black-box LLMs. However,
these methods grapple with the misalignment between the distributions of the
surrogate and the often undisclosed target models, leading to performance
degradation, particularly with the introduction of new, closed-source models.
Furthermore, while current methodologies are generally effective when the
source model is identified, they falter in scenarios where the model version
remains unknown, or the test set comprises outputs from various source models.
To address these limitations, we present Distribution-Aligned LLMs Detection
(DALD), an innovative framework that redefines the state-of-the-art performance
in black-box text detection even without logits from source LLMs. DALD is
designed to align the surrogate model's distribution with that of unknown
target LLMs, ensuring enhanced detection capability and resilience against
rapid model iterations with minimal training investment. By leveraging corpus
samples from publicly accessible outputs of advanced models such as ChatGPT,
GPT-4 and Claude-3, DALD fine-tunes surrogate models to synchronize with
unknown source model distributions effectively.
