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.
Understanding Beam Search
Beam search is a widely used decoding strategy in natural language processing that strikes a practical balance between the greediness of always picking the single most likely next token and the computational impossibility of exploring every possible sequence. The algorithm maintains a fixed number of candidate sequences (the beam width) at each generation step, expanding each candidate with all possible next tokens and then pruning to keep only the top-scoring partial sequences. With a beam width of one, beam search reduces to greedy decoding; with a very large beam width, it approaches exhaustive search. This technique is fundamental to machine translation, text summarization, and text generation in large language models. However, beam search tends to favor shorter, more generic sequences and can produce repetitive text, leading researchers to develop alternatives like nucleus sampling and top-k sampling for more creative and diverse generation in chatbot and creative writing applications.
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.
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.
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.