🤯 Did You Know (click to read)
BERT can extract answers to questions from long text passages by analyzing token-level context bidirectionally.
BERT leverages bidirectional encodings to understand both the question and the context passage. Fine-tuning on QA datasets like SQuAD allows it to extract precise answer spans from passages. Attention mechanisms help the model focus on relevant context, improving response accuracy in reading comprehension and information retrieval tasks.
💥 Impact (click to read)
Contextual QA systems improve information access for education, research, and virtual assistants. Users can receive accurate answers without manually searching entire documents.
For users, BERT provides responses that feel informed and context-aware. The irony is that answers emerge from statistical correlations rather than understanding.
Source
Devlin et al., 2018, BERT: Pre-training of Deep Bidirectional Transformers
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