  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 distortion
compared to the original probability distribution, and no noticeable
degradation in quality. We evaluate our watermark on base and instruction
fine-tuned models and find our watermark is robust to edits, deletions, and
translations. We provide an information-theoretic perspective on watermarking,
a powerful statistical test for detection and for generating p-values, and
theoretical guarantees. Our empirical findings suggest our watermark is fast,
powerful, and robust, comparing favorably to the state-of-the-art.
