arXiv:2010.04335
공식 코드 · Papers with Code
NutCracker at WNUT-2020 Task 2: Robustly Identifying Informative COVID-19 Tweets using Ensembling and Adversarial Training
원문 초록
We experiment with COVID-Twitter-BERT and RoBERTa models to identify informative COVID-19 tweets. We further experiment with adversarial training to make our models robust. The ensemble of COVID-Twitter-BERT and RoBERTa obtains a F1-score of 0.9096 (on the positive class) on the test data of WNUT-2020 Task 2 and ranks 1st on the leaderboard. The ensemble of the models trained using adversarial training also produces similar result.
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The Trilemma of Truth in Large Language Models
Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation
AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios
Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts
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