In-Place Tokenizer Expansion for Pre-trained LLMs
Researchers propose an in-place tokenizer expansion method to address the limitations of pre-trained language models' vocabulary.
- Pre-trained language models face challenges with vocabulary limitations.
- In-place tokenizer expansion can help address these limitations.
- The proposed method is particularly relevant for on-device models.
A recent study highlights the issue of vocabulary limitations in pre-trained language models (LLMs). The current approach to tokenization can lead to increased latency, compute, and energy consumption when languages added later are split into many more tokens per word. This problem is more pronounced in on-device models, where the embedding and LM-head matrices take up a significant share of per-token decode bandwidth. To address this challenge, researchers propose an in-place tokenizer expansion method, which allocates vocabulary in proportion to the pre-training corpus. This approach can help ensure that the vocabulary is more representative of the deployment priorities at the time of deployment.
The proposed method is particularly relevant for on-device models, where the compact size of the model is crucial. By expanding the vocabulary in-place, developers can create more efficient and effective LLMs that can handle a broader range of languages and tasks.
This development has significant implications for the development of AI models, particularly in the areas of natural language processing and machine translation.
This development can help create more efficient and effective LLMs for on-device applications.
The proposed method can lead to cost savings and improved performance in AI-powered applications.
This development has significant implications for the future of AI model development and deployment.
This breakthrough can lead to more efficient and effective language models for various applications.
- in-place tokenizer expansion
- A method to expand the vocabulary of pre-trained language models without retraining them.
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