Transformer models enhance information retrieval (IR) by leveraging their ability to capture long-range dependencies and context in text. Unlike traditional models, transformers process entire input sequences simultaneously, making them highly effective at understanding the meaning behind queries and documents.
For example, in IR systems, transformers like BERT and GPT can better understand complex, ambiguous, or context-dependent queries. Instead of focusing solely on keyword matching, these models analyze the full context of the query to retrieve the most relevant results. This enables a deeper understanding of intent and improves the quality of search results.
Transformers also enable bidirectional context processing, meaning they consider both the preceding and following words in a sentence, enhancing the relevance of retrieved results. This capability has made transformer models a powerful tool in modern search engines, where understanding nuances and context in user queries is crucial for delivering accurate answers.