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Found 5 papers, 1 papers with code
Understanding the Training Speedup from Sampling with Approximate Losses
In this work, we focus on the greedy approach of selecting samples with large \textit{approximate losses} instead of exact losses in order to reduce the selection overhead.
Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost
Instead, we propose a sampling strategy based on the difference in prediction scores between the base model and the finetuned NLP model, utilizing the fact that most NLP models are finetuned from a base model.
Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers
Therefore, using methods from the causal inference literature, we propose an algorithm to regularize the learnt effect of the features on the model's prediction to the estimated effect of feature on label.
Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems
To explain this generalization failure, we consider an intervention-based importance metric, which shows that a fine-tuned model captures spurious correlations and fails to learn the causal features that determine the relevance between any two text inputs.
Missing Value Imputation on Multidimensional Time Series
Missing values are commonplace in decision support platforms that aggregate data over long time stretches from disparate sources, and reliable data analytics calls for careful handling of missing data.
