🤯 Did You Know (click to read)
BERT achieved state-of-the-art results on MNLI by leveraging bidirectional context for sentence pair evaluation.
BERT uses bidirectional transformers and contextual embeddings to perform natural language inference (NLI). Given a premise and a hypothesis, it predicts entailment, contradiction, or neutrality. Fine-tuning on datasets like MNLI enables robust semantic reasoning. This is used in QA, summarization, and content verification.
💥 Impact (click to read)
Textual entailment allows AI to reason about meaning, supporting tasks that require logical comprehension of text relationships.
For users, GPT systems using BERT can assess sentence consistency. The irony is that statistical modeling simulates logical inference without actual understanding.
Source
Devlin et al., 2018, BERT: Pre-training of Deep Bidirectional Transformers
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