Aleksandra Edwards — accepted AI-conference papers

2024

Language Models for Text Classification: Is In-Context Learning Enough?

Aleksandra Edwards, Jose Camacho-Collados

COLING 2024main

Recent foundational language models have shown state-of-the-art performance in many NLP tasks in zero- and few-shot settings. An advantage of these models over more standard approaches based on fine-tuning is the ability to understand instructions written in natural language (prompts), which helps t…

Cited by 35 Source ↗PDF ↗Scholar ↗

2020

Go Simple and Pre-Train on Domain-Specific Corpora: On the Role of Training Data for Text Classification

Aleksandra Edwards, Jose Camacho-Collados, Hélène De Ribaupierre, Alun Preece

COLING 2020main

Pre-trained language models provide the foundations for state-of-the-art performance across a wide range of natural language processing tasks, including text classification. However, most classification datasets assume a large amount labeled data, which is commonly not the case in practical settings…

Cited by 35 Source ↗PDF ↗Scholar ↗