What is a Tokenizer in Large Language Models (LLM)?

A tokenizer in the context of large language models (LLMs) is a tool that splits text into smaller units called tokens (e.g., words, subwords, or characters) for processing by the model. NVIDIA’s NeMo documentation on NLP preprocessing explains that tokenization is a critical step in preparing text data, with algorithms like WordPiece, Byte-Pair Encoding (BPE), or SentencePiece breaking text into manageable units to handle vocabulary constraints and out-of-vocabulary words. For example, the sentence “I love AI” might be tokenized into [“I”, “love”, “AI”] or subword units like [“I”, “lov”, “##e”, “AI”]. Option A is incorrect, as removing stop words is a separate preprocessing step. Option B is wrong, as tokenization is not a predictive algorithm. Option D is misleading, as converting text to numerical representations is the role of embeddings, not tokenization.