LLMs

Large language models (LLMs) are a class of artificial intelligence models that generate or process natural language using deep neural networks with hundreds of millions to trillions of parameters. They are typically built on transformer architectures and trained to predict the next token in text, enabling coherent generation and understanding.

Training involves pretraining on broad text corpora drawn from books, articles, websites, and code, often with multilingual data. The objective is to learn statistical patterns of language, followed by fine-tuning on specific tasks or instruction-following objectives to improve controllability and usefulness.

Capabilities include text completion, summarization, translation, question answering, reasoning, and coding assistance. They can adapt to various styles and domains, but their outputs reflect training data and may contain inaccuracies, biases, or unsafe content if not properly managed.

Limitations and challenges include factual correctness, inconsistent reasoning, sensitivity to prompts, and high computational cost. Mitigation strategies involve safety filters, model alignment, content policies, data curation, evaluation pipelines, and human oversight.

Applications span conversational agents, drafting, knowledge retrieval, programming help, and educational tools. Deployment considerations include latency, cost, privacy, and risk management. The field emphasizes responsible use, ongoing research into model alignment, interpretability, and governance to address misinformation and bias.