Transformers, particularly models like BERT (Bidirectional Encoder Representations from Transformers), enhance information retrieval (IR) by improving the understanding of context and semantics in queries and documents. Unlike traditional models that rely on bag-of-words representations, transformers capture the meaning of words based on their surrounding context, enabling more accurate matching between queries and documents.
In IR, transformers are used to improve relevance by providing deep contextual understanding. For example, when a user submits a query, a transformer model can generate a rich vector representation of the query, which can then be compared to document representations to find the most relevant results. This leads to more accurate semantic search and improved handling of synonyms, polysemy, and complex queries.
Transformers also allow for fine-tuning on domain-specific tasks, making them adaptable for various IR applications, such as question answering, news aggregation, and legal document search. Their ability to understand context and relationships between words significantly improves search quality over traditional models.