  Our work addresses the critical issue of distinguishing text generated by
Large Language Models (LLMs) from human-produced text, a task essential for
numerous applications. Despite ongoing debate about the feasibility of such
differentiation, we present evidence supporting its consistent achievability,
except when human and machine text distributions are indistinguishable across
their entire support. Drawing from information theory, we argue that as
machine-generated text approximates human-like quality, the sample size needed
for detection increases. We establish precise sample complexity bounds for
detecting AI-generated text, laying groundwork for future research aimed at
developing advanced, multi-sample detectors. Our empirical evaluations across
multiple datasets (Xsum, Squad, IMDb, and Kaggle FakeNews) confirm the
viability of enhanced detection methods. We test various state-of-the-art text
generators, including GPT-2, GPT-3.5-Turbo, Llama, Llama-2-13B-Chat-HF, and
Llama-2-70B-Chat-HF, against detectors, including oBERTa-Large/Base-Detector,
GPTZero. Our findings align with OpenAI's empirical data related to sequence
length, marking the first theoretical substantiation for these observations.
