Patronus AI; Introducing CopyrightCatcher, thefirst Copyright Detection API for LLMs "Managing risks from unintended copyright infringement in LLM outputs should be a central focus for companies deploying LLMs in production. On an adversarial copyright test designed by Patronus AI researchers, we found that state-of-the-art LLMs generate copyrighted content at an alarmingly high rate 😱 OpenAI’s GPT-4 produced copyrighted content on 44% of the prompts. Mistral’s Mixtral-8x7B-Instruct-v0.1 produced copyrighted content on 22% of the prompts. Anthropic’s Claude-2.1 produced copyrighted content on 8% of the prompts. Meta’s Llama-2-70b-chat produced copyrighted content on 10% of the prompts. Check out CopyrightCatcher, our solution to detect potential copyright violations in LLMs. Here’s the public demo, with open source model inference powered by Databricks Foundation Model APIs. 🔥 LLM training data often contains copyrighted works, and it is pretty easy to get an LLM to generate exact reproductions from these texts1. It is critical to catch these reproductions, since they pose significant legal and reputational risks for companies that build and use LLMs in production systems2. OpenAI, Anthropic, and Microsoft have all faced copyright lawsuits on LLM generations from authors3, music publishers4, and more recently, the New York Times5. To check whether LLMs respond to your prompts with copyrighted text, you can use CopyrightCatcher. It detects when LLMs generate exact reproductions of content from text sources like books, and highlights any copyrighted text in LLM outputs. Check out our public CopyrightCatcher demo here!

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