The field of large language models (LLMs) is rapidly advancing, with a growing focus on ensuring the integrity and authenticity of generated text. Recent developments have centered on watermarking and auditing techniques, which enable the detection of sensitive or copyrighted content and the attribution of generated text to its source model. These innovations have significant implications for the responsible development and deployment of LLMs, particularly in applications where trust and accountability are paramount. Noteworthy papers in this area have introduced novel watermarking frameworks, such as text-preserving watermarking and semantic key modules, which improve the robustness and detectability of watermarks. Additionally, research has explored the use of linguistic features and geometric constraints to create forgery-resistant signatures for LLMs. These advancements have the potential to enhance the transparency and reliability of LLMs, ultimately contributing to a more trustworthy AI ecosystem. Some particularly noteworthy papers include: DITTO: A Spoofing Attack Framework on Watermarked LLMs via Knowledge Distillation, which demonstrates the vulnerability of watermarking to spoofing attacks. SimKey: A Semantically Aware Key Module for Watermarking Language Models, which introduces a semantic key module to strengthen watermark robustness. Every Language Model Has a Forgery-Resistant Signature, which proposes a novel technique for extracting a forgery-resistant signature from LLM outputs.