Copyright Trap

Copyright trap research focuses on detecting the unauthorized use of copyrighted material in training data for large language models (LLMs). Current work explores methods like embedding "traps"—unique text sequences—into training datasets to identify if LLMs have memorized copyrighted content, investigating variations such as "fuzzy" traps with slight modifications to evade deduplication techniques. This research is crucial for addressing legal and ethical concerns surrounding LLM development, impacting both the commercial viability of AI startups through compliance costs and the broader fairness and transparency of AI systems.