Masked Language Model
A masked language model is a training approach where random tokens in a sentence are hidden and the model learns to predict them from context. BERT popularized masked language modeling as a pre-training objective.
Understanding Masked Language Model
A masked language model is a pre-training approach where a model learns to predict randomly hidden (masked) tokens within a text sequence, developing deep bidirectional understanding of language. BERT pioneered this technique by randomly masking 15% of input tokens and training the model to reconstruct them using surrounding context from both directions. This bidirectional approach gives masked language models strong performance on natural language understanding tasks like sentiment analysis, named entity recognition, and question answering. Unlike autoregressive language models that read text left to right, masked language models process the entire input simultaneously, capturing richer contextual relationships. The pre-trained representations transfer effectively to downstream tasks through fine-tuning, making masked language models a cornerstone of modern natural language processing. Variants like RoBERTa and DeBERTa refined the masking strategy and training procedure to achieve even stronger results.
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.