Большая языковая модель (LLM)
Большая языковая модель (LLM) — это алгоритм глубокого обучения, использующий нейронную сеть для понимания и генерации текста, похожего на человеческий, на основе огромных наборов данных.
A Large Language Model (LLM) is a type of artificial intelligence model, specifically a deep learning algorithm, designed to understand, generate, and manipulate human language. LLMs are built upon deep neural network architectures, most commonly the [Transformer architecture](/ru/terms/transformer-architecture), which utilizes self-attention mechanisms to weigh the importance of different words in a sequence. They are trained on massive datasets of text and code, often comprising billions or even trillions of words, enabling them to learn intricate patterns, grammar, context, and factual knowledge. The training process typically involves unsupervised learning, where the model predicts missing words or the next word in a sequence. This pre-training phase equips the LLM with a broad understanding of language. Subsequently, models can be fine-tuned for specific tasks, such as translation, summarization, question answering, or text generation, using smaller, labeled datasets or through techniques like Reinforcement Learning from Human Feedback (RLHF). The 'large' in LLM refers to both the vast size of the training data and the enormous number of parameters (weights and biases) within the neural network, which can range from millions to trillions. These parameters capture the learned linguistic knowledge.
graph LR
Center["Большая языковая модель (LLM)"]:::main
Pre_artificial_intelligence["artificial-intelligence"]:::pre --> Center
click Pre_artificial_intelligence "/terms/artificial-intelligence"
Pre_machine_learning["machine-learning"]:::pre --> Center
click Pre_machine_learning "/terms/machine-learning"
Rel_ai_agent["ai-agent"]:::related -.-> Center
click Rel_ai_agent "/terms/ai-agent"
Rel_llm["llm"]:::related -.-> Center
click Rel_llm "/terms/llm"
Rel_artificial_intelligence["artificial-intelligence"]:::related -.-> Center
click Rel_artificial_intelligence "/terms/artificial-intelligence"
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🧒 Простыми словами
Это как супер-умный Т9 или попугай, который прочитал весь интернет. Он не понимает мир как мы, но он так круто предсказывает следующее слово, что может поддержать любой разговор или написать программный код.
🤓 Expert Deep Dive
The [Transformer architecture](/ru/terms/transformer-architecture), with its self-attention mechanism, is foundational to modern LLMs. Self-attention allows the model to dynamically compute representations of tokens based on their relationships within the input sequence, overcoming the limitations of recurrent neural networks (RNNs) in handling long-range dependencies. The scale of LLMs, characterized by parameter counts (e.g., GPT-3 with 175 billion parameters) and dataset size (e.g., Common Crawl), directly correlates with emergent capabilities. Training involves optimizing a loss function (e.g., cross-entropy) over vast corpora, often requiring significant computational resources (TPUs/GPUs). Key challenges include mitigating biases present in training data, controlling model hallucinations (generating factually incorrect information), ensuring safety and ethical alignment, and managing the computational cost of inference. Techniques like quantization and knowledge distillation are employed to create smaller, more efficient models.