Large Language Model
A Large Language Model (LLM) is a neural network with billions of parameters trained on massive text datasets to understand and generate human language. LLMs like GPT-4, Claude, and Gemini demonstrate broad capabilities across language tasks.
Understanding Large Language Model
A large language model (LLM) is a neural network with billions of parameters trained on massive text datasets to understand and generate human language with remarkable fluency and versatility. Models like GPT-4, Claude, Gemini, and LLaMA have demonstrated capabilities ranging from creative writing and code generation to complex reasoning and multi-step problem solving. LLMs are built on the transformer architecture and trained using next-token prediction at enormous scale, often requiring thousands of GPUs and months of computation. Fine-tuning techniques like instruction tuning and RLHF align these models to follow human instructions safely. Despite their capabilities, LLMs face challenges including hallucination, bias, and high inference costs. The development of techniques like LoRA, knowledge distillation, and mixture of experts architectures aims to make LLMs more efficient and accessible for diverse 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.
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.