Large language model training

During training, an LLM analyzes massive text datasets to learn how words, phrases and concepts relate to one another. The model predicts the next word in a sequence, gradually learning syntax, semantics and style through statistical probabilities.

However, the model’s performance depends entirely on the quality of its training data. Unstructured or biased inputs can lead to inaccurate, inconsistent or culturally insensitive results. Structured, well-annotated and representative datasets – particularly those enriched with metadata – enable the model to generate precise, context-aware responses. Through TrainAI, RWS combines human linguistic expertise with intelligent automation to prepare, clean and annotate high-quality multilingual data for LLM training. This ensures models learn from accurate, domain-specific and ethically sourced data that reflects real-world linguistic diversity.

At RWS, we view LLM training as a fusion of human insight and intelligent automation. Through TrainAI, our teams curate, clean and annotate multilingual datasets that make AI systems more accurate, inclusive and secure.

By leveraging structured content frameworks – such as DITA – and semantic enrichment, RWS helps organizations train large language models on reliable, componentized data. The result is smarter, context-aware AI that can extract meaning, reason effectively and deliver language output with precision and cultural relevance.