The paper explores the innovative introduction of functional tokens, paralleling their significance in language modeling to linguistic tokens. It proposes a training methodology inspired by the word2vec framework, enabling language models to effectively learn and utilize specialized terms and actions. This approach allows for the representation of specific functions within models, emphasizing that functional tokens can be created without limitation. The research integrates findings across various domains, highlighting the potential for wider application of functional tokens in machine learning and natural language processing environments.
The introduction of functional tokens redefines how specific actions are represented in language models, paralleling the way linguistic tokens encapsulate meaning in natural language.
This new methodology builds on strategies for training rare words in natural language processing, allowing models to learn and effectively utilize functional tokens efficiently.
Pretrained models like the word2vec framework provide a foundation for contextual learning of specialized terms, paving the way for enhanced representation of functional tokens.
Our experiments show no limits on defining functional tokens, empowering users to assign meaningful representations directly linked to specific actions within the model.
#functional-tokens #natural-language-processing #machine-learning #causal-language-models #tokenization
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