SubwordTokenisierungsmethoden

Subword tokenization is a family of methods used in natural language processing to split text into units smaller than whole words. The goal is to reduce the vocabulary size required for models while still enabling the representation of rare or unseen words by combining known subword units. This approach improves robustness to morphology, compounding, and multilingual variation, and it helps models generalize from seen data to new word forms.

Common techniques include Byte Pair Encoding (BPE), WordPiece, and Unigram language model-based tokenization (as implemented in SentencePiece). These methods operate on a fixed vocabulary of subword units and define rules for how text should be segmented during preprocessing. BPE starts with individual characters and iteratively merges the most frequent adjacent pairs; WordPiece builds a probabilistic boundary model; Unigram treats tokenization as a search over a set of subword units to maximize likelihood.

Advantages include reduced out-of-vocabulary incidence, better handling of morphologically rich languages, and more compact vocabularies that still cover productive word forms. Disadvantages include longer token sequences in some cases, potential loss of semantic clarity at the subword level, and the need for careful, language-aware training data and pre-processing to ensure consistent tokenization.

Subword tokenization is standard in training large language models and multilingual systems, enabling efficient handling of diverse vocabularies and code-switching. It remains an area of ongoing research, with efforts to optimize boundary quality, language-agnostic approaches, and dynamic or byte-level variants.