Description: The ‘Upstream Training’ refers to an approach in the development of large language models (LLMs) that occurs before making specific adjustments to the model. This process involves the initial training of the model using a broad and diverse dataset, allowing the model to learn general patterns of language, grammar, context, and meaning. During this phase, the model is exposed to a variety of texts, helping it build a solid foundation of linguistic knowledge. The goal of upstream training is to equip the model with a general understanding of language, which can then be refined through additional tuning, known as ‘fine-tuning’, where the model is trained on specific tasks or with more concrete data. This approach is crucial for maximizing the model’s effectiveness in practical applications, as it enables the model to not only recognize words and phrases but also understand the context and intent behind them. In summary, upstream training is a fundamental stage in creating robust and versatile language models that lay the groundwork for their subsequent specialization and application in various natural language processing tasks.
