Large language models (LLMs) have revolutionized how we interact with technology, demonstrating impressive abilities in understanding and generating human-like text. However, their speed has been a persistent bottleneck, especially for real-time applications. Imagine a conversation with an AI that takes several seconds to respond after each of your prompts—not exactly ideal. This lag is due to the sequential nature of traditional decoding methods, where LLMs predict and generate one word at a time. But what if they could predict multiple words simultaneously? New research introduces a groundbreaking approach called Lexical Unit Decoding (LUD), which allows LLMs to do just that. LUD leverages the observation that LLMs often predict certain sequences of words—like common phrases or code blocks—with high confidence. By identifying these "lexical units," LUD enables the model to generate entire chunks of text in parallel, significantly speeding up the decoding process. The key innovation lies in training the model to recognize and predict these lexical units. Researchers first fine-tune a pre-trained LLM on a target dataset, then analyze its predictions to identify sequences of words consistently predicted with high probability. These sequences become the lexical units. The model is then retrained to predict these units as single chunks, rather than individual words. The results are impressive. Experiments show LUD accelerates text generation by 33% and code generation by 30% with minimal impact on quality. This means faster responses in chatbots, quicker code completion, and more efficient text generation overall. Interestingly, the acceleration is even more pronounced for code generation, likely because code often follows stricter patterns and structures than natural language. This suggests LUD adapts to the predictability of the content, further optimizing the decoding process. While LUD offers a significant leap forward, challenges remain. Identifying lexical units effectively is crucial, and more sophisticated methods could further improve performance. Also, the model's understanding of lexical units might evolve over time, necessitating dynamic adaptation. Despite these challenges, LUD represents a paradigm shift in LLM decoding, paving the way for truly real-time AI interactions and unlocking new possibilities across various applications.