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.
Understanding BERT
BERT, or Bidirectional Encoder Representations from Transformers, fundamentally changed the landscape of natural language processing when Google released it in 2018 by demonstrating that pre-training a deep bidirectional transformer on massive text corpora could produce universally useful language representations. Unlike previous models that read text left-to-right or right-to-left, BERT's masked language modeling objective trains the model to predict randomly hidden words based on their full surrounding context in both directions. This bidirectional understanding allows BERT to capture nuanced meanings that depend on both preceding and following words. After pre-training, BERT can be fine-tuned with relatively small amounts of labeled data for tasks like classification, question answering, and named entity recognition. BERT's architecture built directly on the attention mechanism and transformer innovations, and its success spawned a family of variants including RoBERTa, DistilBERT, and ALBERT. BERT also powers Google Search's understanding of queries, demonstrating its massive real-world impact.
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.
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.
Language Model
A language model is an AI system that learns the probability distribution of sequences of words in a language. Modern language models like GPT and Claude can generate text, answer questions, and perform complex reasoning.