Large language model (LLM)
Description
Large language models represent a significant leap forward in Natural Language Processing (NLP). Unlike earlier AI models that were designed for specific, narrow tasks, LLMs are "general purpose" engines. They are built using transformer architectures – a type of neural network that allows the model to track relationships between words across long distances in a text, giving them a deep understanding of context and meaning.
The "large" in LLM refers to two things: the size of the training dataset (often petabytes of text from the internet, books and articles) and the number of parameters (the internal variables the model uses to make predictions, often numbering in the billions or trillions). Through a process called self-supervised learning, the model analyzes this data to learn the statistical structure of language – predicting the next word in a sentence until it can construct coherent paragraphs and arguments. However, raw LLMs can be unpredictable. They may "hallucinate" (invent facts), exhibit bias or lack domain-specific knowledge. To make them useful for enterprise applications, they require fine-tuning and alignment. This involves training the model on smaller, high-quality datasets (such as proprietary company data or verified translations) and using techniques like Human-in-the-Loop (HITL) feedback to guide its behavior.
Example use cases
- Content: Draft marketing copy, technical documentation and social media posts.
- Translation: Power advanced translation engines that can adapt to specific brand tones.
- Summarization: Condense long reports, meetings or legal documents into summaries.
- Chatbots: Drive sophisticated virtual assistants that handle complex queries.
- Coding: Assist developers by writing, debugging and documenting code snippets.
Key benefits
RWS perspective
At RWS, we believe that Large Language Models are only as smart as the data they learn from and the humans who guide them. While LLMs offer incredible speed and fluency, they lack the cultural nuance, accountability and empathy of human intelligence.
We help organizations harness LLMs safely and effectively through a Human + Technology approach. TrainAI provides the high-quality, human-annotated data needed to train and fine-tune models, reducing bias and hallucination. Tridion supplies the structured, semantic content that allows LLMs to access accurate, governed information rather than scraping unverified data. Language Weaver and Evolve apply LLM capabilities to translation and localization, ensuring that AI-generated content is validated by expert linguists.