Generative models in information retrieval (IR) are used to generate new content or enhance existing content to improve the search experience. Unlike discriminative models, which focus on classifying or ranking data, generative models create new data based on learned patterns from existing information.
In IR, generative models can be used for tasks such as query generation, document summarization, and content generation. For instance, in a question-answering system, a generative model like GPT can generate answers to user queries by producing relevant content that is both coherent and contextually appropriate.
Generative models can also help improve query expansion, where the model generates additional terms or phrases related to the user's original query. This helps improve the retrieval process by broadening the search scope while maintaining relevance, resulting in more comprehensive and precise search results.