Abstract
A primary concern regarding training large language models (LLMs) is whether they abuse copyrighted online text. With the increasing training data scale and the prevalence of LLMs in daily lives, two problems arise: \textbf\{1)\} false positive membership inference results misled by similar examples; \textbf\{2)\} membership inference methods are usually too complex for end users to understand and