During the talk we will cover:

1. Why Watermarking Matters?
   • What can go wrong when AI-generated content becomes indistinguishable from human writing
   • Why provenance and transparency are becoming essential to trust and safety.
2. How LLM Watermarking Works?
   • What is a watermark and what isn't
   • The core idea behind statistical watermarking
3. Two Key Algorithms implemented using Python's established frameworks
   • EXP Watermark: modifying logits with pseudo-random perturbations.
   • KGW Green-List Watermark: partitioning tokens into “green” and “red” lists to bias sampling.
   • Python code walkthrough of implementing the KGW method and comparing it with the EXP method.
4. How you can use MarkLLM (open-source toolkit)
   • Generate and detect watermarked text.
   • How to use the toolkit for experiments in your own workflows.
5. Real-World Challenges and Limitations
   • How robust and evasive are the current algorithms

Key Takeaways:

 - Watermarking is a promising tool for provenance.
 - Developers can implement and test watermarking fully in Python.
 - Understanding these methods helps build more transparent and trustworthy AI systems.

This talk is for people who:

• Care about ethics and privacy in AI and want to understand what watermarking can (and cannot) solve.
• Build applications using LLMs and want mechanisms for verifying or auditing generated text.
• Are ML researchers or hobbyists interested in how watermarking algorithms function at a technical level.
• Work in AI safety, trust & transparency, or responsible AI and need practical tools for content provenance.

Note: No prior experience with LLM architecture is required, basic familiarity with probability is recommended; no advanced math needed.