Tokenization
Tokenization is the process of splitting text into tokens that a language model can process. Modern tokenizers like BPE and SentencePiece balance vocabulary size with the ability to represent any text sequence.
Understanding Tokenization
Tokenization is the process of breaking raw text into smaller units called tokens that serve as the input vocabulary for language models. Modern tokenization algorithms like Byte Pair Encoding, WordPiece, and SentencePiece operate at the subword level, balancing vocabulary size with the ability to represent any text including rare words and multiple languages. Effective tokenization directly impacts model performance, training efficiency, and multilingual capabilities. A well-designed tokenizer produces consistent, meaningful units that help the model learn linguistic patterns efficiently. Tokenization also determines the effective context length of a model, since context windows are measured in tokens rather than words or characters. The choice of tokenizer is tightly coupled with model architecture and pre-training, meaning that each large language model family typically has its own specific tokenizer.
Category
Natural Language Processing
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Abstractive Summarization
Abstractive summarization generates new text that captures the key points of a longer document, rather than simply extracting existing sentences. It requires deep language understanding and generation capabilities.
Beam Search
Beam search is a decoding algorithm that explores multiple candidate sequences simultaneously, keeping only the top-k most promising at each step. It balances between greedy decoding and exhaustive search in text generation.
BERT
BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that reads text in both directions simultaneously. BERT revolutionized NLP by enabling deep bidirectional pre-training for language understanding tasks.
Bigram
A bigram is a contiguous sequence of two items (typically words or characters) from a given text. Bigram models estimate the probability of a word based on the immediately preceding word.
Byte Pair Encoding
Byte Pair Encoding (BPE) is a subword tokenization algorithm that iteratively merges the most frequent pairs of characters or character sequences. BPE is widely used in modern language models to handle rare words and multilingual text.
Corpus
A corpus is a large, structured collection of text documents used for training and evaluating natural language processing models. The quality and diversity of a training corpus significantly impacts model performance.
Extractive Summarization
Extractive summarization selects and combines the most important sentences directly from a source document to create a summary. It preserves the original wording but may lack the coherence of abstractive approaches.
Grounding
Grounding in AI refers to connecting a model's language understanding to real-world knowledge, data, or sensory experience. Grounded AI systems produce more factual and contextually relevant outputs.