Watermarking Language Models with Error Correcting Codes

Understand

Recent progress in large language models enables the creation of realistic machine-generated content.

• Watermarking is a promising approach to distinguish machine-generated text from human text, embedding statistical signals in the output that are ideally undetectable to humans.
• We propose a watermarking framework that encodes such signals through an error correcting code.
• Our method, termed robust binary code (RBC) watermark, introduces no noticeable degradation in quality.