Natural Language Processing (NLP)

Natural language processing (NLP) is the branch of artificial intelligence concerned with the interaction between computers and human language. It encompasses the full spectrum from foundational linguistics tasks like tokenization, part-of-speech tagging, and named entity recognition to complex applications like machine translation, question answering, sentiment analysis, and dialogue systems.

NLP has a long history predating the current era of large language models. Early approaches used hand-crafted linguistic rules and statistical methods that were powerful within their domains but brittle when faced with language's ambiguity, context-dependence, and constant evolution. The shift to neural approaches, culminating in the transformer architecture and large pre-trained models, dramatically expanded the scope of what NLP systems could reliably do.

Modern NLP is largely synonymous with large language model applications. Tasks that once required separate specialized models, such as translation, summarization, sentiment analysis, and named entity recognition, can now be handled by a single large language model prompted appropriately. This unification has simplified deployment but concentrated capability in a small number of foundational models.

Core NLP challenges that remain active research areas include coreference resolution (understanding that 'he' and 'John' refer to the same person in context), commonsense reasoning (understanding implicit background knowledge), multilingual generalization (performing well across languages with less training data), and long-document understanding (maintaining coherence across very long contexts).

For AI trainers, most evaluation work is applied NLP: judging whether a model correctly understood the linguistic intent of a prompt and produced a response that is relevant, accurate, and well-formed.