Natural Language Generation
Natural Language Generation (NLG) is the AI task of producing coherent, human-readable text from structured data or prompts. Large language models have made NLG remarkably fluent and contextually appropriate.
Understanding Natural Language Generation
Natural Language Generation (NLG) is the subfield of artificial intelligence focused on producing coherent, contextually appropriate human language from structured data or other inputs. Unlike simple template-based approaches, modern NLG systems powered by large language models can generate creative stories, detailed reports, marketing copy, and code documentation that reads naturally. GPT-4, Claude, and Gemini represent the frontier of NLG capability, leveraging transformer architectures trained on massive text corpora through pre-training and fine-tuning stages. Real-world applications include automated journalism, chatbot responses, personalized email generation, and data-to-text reporting in business intelligence. The quality of NLG output depends heavily on prompt engineering, decoding strategies like beam search, and evaluation metrics such as perplexity and human preference ratings gathered through reinforcement learning from human feedback.
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.