Tokenbasedness

Tokenbasedness is the degree to which a system represents and processes information as discrete tokens. A token is an atomic unit with identity and a position in a sequence. Tokenbasedness describes design choices, data representations, and interfaces that rely on tokenization, as opposed to continuous or wholly holistic representations.

Domains and manifestations include natural language processing, compiler and interpreter design, data serialization and messaging, authentication systems, and certain blockchain or smart contract contexts. In NLP and programming, tokenbasedness follows a tokenization step that converts raw input into tokens before further analysis or execution. In security, token-based approaches use opaque tokens to convey identity or permissions.

Implications and trade-offs:

- Advantages: improved interpretability, modularity, and composability; clearer boundary delineation between components; easier auditing and testing; compatibility with human-readable interfaces.

- Limitations: sensitivity to tokenization decisions and vocabulary choices; potential information loss through coarse or inappropriate token granularity; large vocabularies can increase memory and compute requirements; difficulties handling out-of-vocabulary items or multilingual data.

Measurement and examples:

- Metrics may include token diversity, token entropy, vocabulary size, and the proportion of processing steps that operate on tokens.

- Examples range from a lexer producing tokens for a compiler, to a language model operating on subword tokens, to a token-based authentication flow that uses session tokens.

See also: tokenization, lexical analysis, discontinuous representations, token-based authentication.