AdaDetectGPT: Adaptive Detection of LLM-Generated Text with Statistical Guarantees
This addresses the need for reliable detection of AI-generated text, which is crucial for applications like content moderation and academic integrity, representing an incremental improvement over existing methods.
The paper tackles the problem of detecting whether text is generated by a large language model or a human, introducing AdaDetectGPT, which improves state-of-the-art detection performance by up to 37% across various datasets and LLMs.
We study the problem of determining whether a piece of text has been authored by a human or by a large language model (LLM). Existing state of the art logits-based detectors make use of statistics derived from the log-probability of the observed text evaluated using the distribution function of a given source LLM. However, relying solely on log probabilities can be sub-optimal. In response, we introduce AdaDetectGPT -- a novel classifier that adaptively learns a witness function from training data to enhance the performance of logits-based detectors. We provide statistical guarantees on its true positive rate, false positive rate, true negative rate and false negative rate. Extensive numerical studies show AdaDetectGPT nearly uniformly improves the state-of-the-art method in various combination of datasets and LLMs, and the improvement can reach up to 37\%. A python implementation of our method is available at https://github.com/Mamba413/AdaDetectGPT.