Introducing CopyrightCatcher, the first Copyright Detection API for LLMs

Companies deploying large language models (LLMs) must prioritize managing the risks of unintended copyright infringement in their outputs, as research shows these models frequently reproduce copyrighted content. A study by Patronus AI found that state-of-the-art LLMs, including OpenAI's GPT-4, Mistral's Mixtral-8x7B-Instruct-v0.1, Anthropic's Claude-2.1, and Meta's Llama-2-70b-chat, generated copyrighted content at varying rates, with GPT-4 doing so on 44% of prompts. This presents significant legal and reputational risks, as evidenced by copyright lawsuits against companies like OpenAI, Anthropic, and Microsoft. The study used an adversarial copyright test with prompts derived from copyrighted books to evaluate the models' propensity to reproduce copyrighted material. Results showed that models often generated exact reproductions, which could potentially violate copyright laws, though determining such violations can be complex due to fair use provisions. Tools like CopyrightCatcher can help detect these reproductions, highlighting the need for companies to implement strategies to mitigate infringement risks.

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