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🤯 Did You Know (click to read)
BERT’s passage ranking approach improved Google’s search results by better matching queries to relevant passages.
BERT generates contextual embeddings for both queries and candidate passages. By comparing embeddings with similarity metrics, it ranks passages according to relevance. Fine-tuning on datasets like MS MARCO improves retrieval performance for search engines and QA systems.
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💥 Impact (click to read)
Passage ranking improves search accuracy and content discovery, benefiting users in research, education, and enterprise knowledge management.
For users, ranked results appear contextually precise. The irony is that ranking is based on embedding similarity rather than true comprehension.
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