How do LLMs handle out-of-vocabulary (OOV) words or tokens?
QQuestion
How Large Language Models (LLMs) handle out-of-vocabulary (OOV) words or tokens?
AAnswer
Large Language Models (LLMs) handle out-of-vocabulary (OOV) words or tokens by leveraging subword tokenization techniques such as Byte-Pair Encoding (BPE) or WordPiece. These methods break down rare or unseen words into smaller, more frequent subword units or even characters. This allows the model to generate embeddings for the components of an OOV word and still understand its context and meaning.
By doing so, LLMs can handle a vast vocabulary efficiently without needing to explicitly know every possible word.
EExplanation
To manage out-of-vocabulary (OOV) words, Large Language Models (LLMs) utilize subword tokenization techniques like Byte-Pair Encoding (BPE) or WordPiece. These techniques are crucial for ensuring that LLMs can process and understand words they have not explicitly been trained on.
Theoretical Background
- Byte-Pair Encoding (BPE): This method starts with a basic set of characters and iteratively merges the most frequent adjacent character pairs to form subwords. This results in a vocabulary that includes whole words, subwords, and characters.
- WordPiece: Similar to BPE, WordPiece builds its vocabulary by iteratively adding the most frequent subword units. It is widely used in models like BERT.
Both methods allow the model to break an OOV word into known subwords or characters, which can then be processed to produce meaningful embeddings. This is based on the assumption that even if a word is unseen, its components can convey useful information.
Practical Applications
Subword tokenization allows LLMs to:
- Handle morphologically rich languages efficiently.
- Deal with typos or creative spellings.
- Process technical terms or jargon that might not be present in the training data.
Example
Consider the OOV word "unicorns." An LLM might tokenize it as "uni" + "corn" + "s," where "uni" and "corn" are subwords that the model understands, and "s" is a common suffix.
Diagram
Here's a simple diagram illustrating BPE:
graph LR A["Starting Characters"] --> B["Merge Frequent Pairs"] B --> C["Subword Units"] C --> D["OOV Word Representation"]
External References
- Byte-Pair Encoding (BPE): For more details, you can refer to the original paper by Sennrich et al. (2015).
- WordPiece: Check out the WordPiece to understand its tokenization approach.
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