Before large language models (LLMs) can process text, they split it into chunks such as words or word fragments. The LLM behind ChatGPT, for example, splits “LMU München” into the three chunks “LM,” “U” and “München,” so it never directly sees the individual letters in “München.” This step, which is known as subword tokenization, makes LLMs efficient but causes a range of problems, such as limited character-level understanding.Before large language models (LLMs) can process text, they split it into chunks such as words or word fragments. The LLM behind ChatGPT, for example, splits “LMU München” into the three chunks “LM,” “U” and “München,” so it never directly sees the individual letters in “München.” This step, which is known as subword tokenization, makes LLMs efficient but causes a range of problems, such as limited character-level understanding.Computer Sciences[#item_full_content]