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Found 6 papers, 2 papers with code
Principles from Clinical Research for NLP Model Generalization
The NLP community typically relies on performance of a model on a held-out test set to assess generalization.
Effects of Human Adversarial and Affable Samples on BERT Generalization
BERT-based models have had strong performance on leaderboards, yet have been demonstrably worse in real-world settings requiring generalization.
Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT
However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision.
Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation
Public datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP).
Memorization vs. Generalization: Quantifying Data Leakage in NLP Performance Evaluation
Public datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP).
Assigning function to protein-protein interactions: a weakly supervised BioBERT based approach using PubMed abstracts
Motivation: Protein-protein interactions (PPI) are critical to the function of proteins in both normal and diseased cells, and many critical protein functions are mediated by interactions. Knowledge of the nature of these interactions is important for the construction of networks to analyse biological data.
