languagemodels

Language models are computational models that assign probabilities to sequences of words. They are used to predict the next word, generate coherent text, translate languages, summarize content, or answer questions. Early approaches relied on statistical n-grams with smoothing. Such models captured local dependencies but struggled with long-range context and large vocabularies.

Neural language models, particularly those based on Transformer architectures, have become standard. Autoregressive transformers generate text token by token, conditioned on preceding tokens. Bidirectional or encoder–decoder variants are common for tasks like understanding and translation. Large-scale pretraining on vast corpora, followed by task-specific fine-tuning, is a common paradigm.

Training typically uses self-supervised objectives, such as predicting the next token or reconstructing masked information, across web pages, books, and other sources. Models with hundreds of millions to hundreds of billions of parameters learn rich linguistic representations but require substantial compute and data. Evaluation combines intrinsic metrics like perplexity with extrinsic task benchmarks and human assessments.

Applications include conversational agents, writing assistants, code generation, translation, search, and content summarization. They can also empower tools that assist programming, data analysis, and creative tasks. Responsible deployment often involves safeguards, monitoring, and alignment techniques to reduce unsafe or biased outputs.

Limitations include data-driven biases, tendency to produce plausible but incorrect results (hallucinations), privacy concerns, and substantial energy and resource use. Copyright and data licensing issues also arise when training on proprietary material. Ongoing research addresses reliability, safety, interpretability, and fairness.