🤯 Did You Know (click to read)
BERT achieved top scores on SQuAD v1.1, significantly surpassing prior models in extractive question answering accuracy.
BERT was trained and fine-tuned on datasets like the Stanford Question Answering Dataset (SQuAD), allowing it to extract precise answers from passages of text. Bidirectional context understanding and transformer encoders enable it to locate answers even in complex sentence structures, improving question answering accuracy compared to previous models.
💥 Impact (click to read)
Enhanced QA systems enable faster and more reliable information retrieval in educational, research, and enterprise applications. Users can get precise answers from large documents and datasets without manual reading.
For users, GPT-4 and BERT-based systems provide human-like comprehension. The irony is that statistical models locate answers without understanding content in a human sense.
Source
Devlin et al., 2018, BERT: Pre-training of Deep Bidirectional Transformers
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