How Relevance Models Foreshadowed Transformers for NLP

The revolutionary Transformer architecture, central to modern generative AI, builds upon foundational concepts from earlier Information Retrieval systems. A key intellectual link is the powerful idea of "attention," a probabilistic weighting mechanism that both technologies use to determine relevance. Relevance Models from the early 2000s pioneered this by estimating which words were most likely to co-occur with a search query to create a model of relevant terms. Transformers adapt this same principle on a larger scale, using attention to weigh the importance of every word in a sequence to establish deep contextual meaning.