Retrieval-based models operate by searching through a database of previously stored interactions (questions and answers, for instance) and finding the best match for the input query based on similarity measures. Unlike generative models, which can produce new responses by processing the input through complex neural networks, retrieval-based models are limited to the responses they have in their database, making them more predictable and often more reliable in contexts where accuracy of information is crucial. They are widely used in chatbots and virtual assistants for customer service, where the range of possible queries can be anticipated and a curated set of accurate responses can be prepared in advance. These models often employ techniques like keyword matching, semantic similarity measures, and machine learning algorithms to improve the relevance of their selected responses.
Historical overview: The concept of retrieval-based models has been around since the early days of computer science, with roots in information retrieval and database querying systems. However, their application in NLP and conversational AI became prominent in the 2000s, as advances in machine learning and the availability of large datasets made it possible to implement more sophisticated similarity measures and retrieval mechanisms.
Key contributors: While it's challenging to attribute the development of retrieval-based models to specific individuals due to its foundational nature in computer science and AI, the field of information retrieval has been significantly shaped by pioneers like Gerard Salton, who is known for his work in the 1960s and 1970s on the vector space model and the term frequency-inverse document frequency (TF-IDF) weighting scheme, which are critical to the development of modern retrieval-based systems.