Catch the LLM if you Can: Watermarking LLMs

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 implementation of the KGW method and comparing it with the EXP method. 4. How you can use MarkLLM (open-source toolkit) - 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. - 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 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.