BiMarker introduces a novel bipolar watermarking technique that significantly improves detection of LLM-generated content while maintaining low false positive rates.

• Splits watermarked text into positive and negative poles for enhanced signal detection
• Overcomes limitations of existing watermarking methods that rely on coarse estimates
• Provides more reliable identification of AI-generated text in security-critical applications
• Addresses growing concerns about distinguishing between human and AI-created content

This research advances digital content authentication capabilities, crucial for mitigating risks of AI-generated misinformation, academic dishonesty, and unauthorized content creation.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks