Detecting Pretraining Text Usage in Large Language Models Using Semantic Echo Analysis

Determining whether a piece of text was used to pretrain a large language model (LLM) is a critical challenge in understanding model behavior and ensuring data privacy. In this paper, I propose a novel Semantic Echo Analysis approach to detect pretraining text usage by analyzing the LLM’s output for semantic and stylistic "echoes" of the input text. My method is manual, requiring no access to the model’s internals, and leverages statistical and linguistic analysis to identify overfamiliarity in the LLM’s responses. I compare my approach to existing methods like membership inference attacks, watermarking, and text memorization detection, highlighting its unique focus on semantic patterns. A detailed experimental evaluation, theoretical analysis, and practical insights demonstrate the feasibility of my method for academic and ethical applications, such as data privacy audits and intellectual property protection.