Papers with source training

1 papers
Source-Free Unsupervised Domain Adaptation for Question Answering via Prompt-Assisted Self-learning (2024.findings-naacl)

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Challenge: Existing SFDA methods focus on the adaptation phase, overlooking the impact of source domain training on model generalizability.
Approach: They propose a source-free domain adaptation approach for Question Answering where a model trained on a domain is adapted to unlabeled target domains without additional source data.
Outcome: The proposed model outperforms existing methods in managing domain gaps and demonstrating greater stability across target domains.

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