Pre-trained models like BERT play a crucial role in modern information retrieval (IR) by improving the system's understanding of language and context. BERT (Bidirectional Encoder Representations from Transformers) is trained on vast amounts of text and is capable of understanding context in a bidirectional manner, meaning it can interpret words based on the surrounding words, rather than just their immediate neighbors.
In IR, BERT is used to improve query understanding and document relevance ranking. By embedding queries and documents into high-dimensional vectors, BERT can capture semantic relationships and context, allowing the IR system to match queries with documents that are contextually relevant, even if they don't share exact terms.
Pre-trained models like BERT reduce the need for feature engineering, as they can directly generate embeddings that capture the meaning of words, sentences, and even entire documents. This leads to better search quality, especially in tasks like semantic search, question answering, and content recommendation, where understanding the intent behind the query is key to providing relevant results.