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洞見 - Unsupervised Pretraining for Fact Verification
Unsupervised Pretraining for Fact Verification by Language Model Distillation at ICLR 2024
Unsupervised pretraining framework SFAVEL achieves state-of-the-art results in fact verification by distilling language model features.
Unsupervised Pretraining for Fact Verification by Language Model Distillation: A Novel Approach
The author proposes SFAVEL, an unsupervised pretraining framework leveraging language model distillation to achieve high-quality claim-fact alignments without annotations. Results show state-of-the-art performance on fact verification datasets.
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