What is a pre-trained language model?

A pre-trained language model (PLM) is a type of artificial intelligence system designed to understand and generate human language by learning patterns from vast amounts of text data during an initial training phase. These models are built using neural networks, which analyze sequences of words to predict the likelihood of the next word in a sentence. The “pre-trained” aspect means the model is first trained on general-purpose text (e.g., books, websites, or articles) to develop broad language understanding. Developers can later fine-tune these models for specific tasks like translation, summarization, or question answering, saving time and resources compared to training from scratch[7].

The training process involves two main stages. First, the model learns through unsupervised learning, where it identifies relationships between words and phrases without explicit labels. For example, it might learn that “Paris” is associated with “France” or that “rain” often follows “cloudy.” Second, during fine-tuning, the model adapts to specialized tasks using smaller, labeled datasets. Popular architectures like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) use this approach. BERT, for instance, excels at understanding context in both directions (left and right of a word), making it effective for tasks like sentiment analysis[7].

A key example of PLMs in practice is OpenAI’s InstructGPT, which improves upon GPT-3 by incorporating human feedback during training to better align outputs with user intentions[7]. This adjustment helps reduce harmful or nonsensical responses while maintaining the model’s ability to handle diverse queries. PLMs power tools like chatbots, code autocompletion systems, and content moderation filters. Their versatility stems from their foundational training, which captures grammar, facts, and reasoning patterns, enabling adaptation to niche applications without requiring massive task-specific datasets.

[7] Aligning language models to follow instructions