Katerina Margatina | DeepAI

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∙ 05/23/2023

Active Learning Principles for In-Context Learning with Large Language Models

The remarkable advancements in large language models (LLMs) have signifi...

7 Katerina Margatina, et al. ∙

∙ 05/21/2023

On the Limitations of Simulating Active Learning

Active learning (AL) is a human-and-model-in-the-loop paradigm that iter...

2 Katerina Margatina, et al. ∙

∙ 02/23/2023

Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views

Temporal concept drift refers to the problem of data changing over time....

0 Katerina Margatina, et al. ∙

∙ 02/14/2023

Investigating Multi-source Active Learning for Natural Language Inference

In recent years, active learning has been successfully applied to an arr...

1 Ard Snijders, et al. ∙

∙ 03/18/2022

Challenges and Strategies in Cross-Cultural NLP

Various efforts in the Natural Language Processing (NLP) community have ...

12 Daniel Hershcovich, et al. ∙

∙ 09/08/2021

Active Learning by Acquiring Contrastive Examples

Common acquisition functions for active learning use either uncertainty ...

14 Katerina Margatina, et al. ∙

∙ 09/04/2021

Frustratingly Simple Pretraining Alternatives to Masked Language Modeling

Masked language modeling (MLM), a self-supervised pretraining objective,...

1 Atsuki Yamaguchi, et al. ∙

∙ 04/16/2021

Bayesian Active Learning with Pretrained Language Models

Active Learning (AL) is a method to iteratively select data for annotati...

30 Katerina Margatina, et al. ∙

∙ 09/28/2020

Domain Adversarial Fine-Tuning as an Effective Regularizer

In Natural Language Processing (NLP), pre-trained language models (LMs) ...

6 Giorgos Vernikos, et al. ∙

∙ 06/09/2019

Attention-based Conditioning Methods for External Knowledge Integration

In this paper, we present a novel approach for incorporating external kn...

0 Katerina Margatina, et al. ∙

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