As large language models (LLM) are increasingly used for text generation tasks, it is critical to audit their usages, govern their applications, and mitigate their potential harm. This need may also be reinforced by regulatory activities. Ideally, the generated output of LLMs should carry machine-detectable patterns (i.e., watermarks) without significantly affecting generated text quality and semantics. This chapter provides an overview of watermarking techniques for LLMs and discuss their efficiency in watermark detection and robustness against post-editing.