A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities
Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …
Overview of autextification at iberlef 2023: Detection and attribution of machine-generated text in multiple domains
AM Sarvazyan, JÁ González… - arXiv preprint arXiv …, 2023 - arxiv.org
This paper presents the overview of the AuTexTification shared task as part of the IberLEF
2023 Workshop in Iberian Languages Evaluation Forum, within the framework of the SEPLN …
2023 Workshop in Iberian Languages Evaluation Forum, within the framework of the SEPLN …
Mask-guided BERT for few-shot text classification
Transformer-based language models have achieved significant success in various domains.
However, the data-intensive nature of the transformer architecture requires much labeled …
However, the data-intensive nature of the transformer architecture requires much labeled …
Overview of pan 2023: Authorship verification, multi-author writing style analysis, profiling cryptocurrency influencers, and trigger detection: Condensed lab overview
J Bevendorff, I Borrego-Obrador, M Chinea-Ríos… - … Conference of the Cross …, 2023 - Springer
The paper gives a brief overview of three shared tasks which have been organized at the
PAN 2023 lab on digital text forensics and stylometry hosted at the CLEF 2023 conference …
PAN 2023 lab on digital text forensics and stylometry hosted at the CLEF 2023 conference …
Crass: A novel data set and benchmark to test counterfactual reasoning of large language models
J Frohberg, F Binder - arXiv preprint arXiv:2112.11941, 2021 - arxiv.org
We introduce the CRASS (counterfactual reasoning assessment) data set and benchmark
utilizing questionized counterfactual conditionals as a novel and powerful tool to evaluate …
utilizing questionized counterfactual conditionals as a novel and powerful tool to evaluate …
Active few-shot learning with fasl
Recent advances in natural language processing (NLP) have led to strong text classification
models for many tasks. However, still often thousands of examples are needed to train …
models for many tasks. However, still often thousands of examples are needed to train …
Extreme zero-shot learning for extreme text classification
The eXtreme Multi-label text Classification (XMC) problem concerns finding most relevant
labels for an input text instance from a large label set. However, the XMC setup faces two …
labels for an input text instance from a large label set. However, the XMC setup faces two …
[HTML][HTML] Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks: Algorithm Development and Validation Study
Background Natural language processing (NLP) has become an emerging technology in
health care that leverages a large amount of free-text data in electronic health records to …
health care that leverages a large amount of free-text data in electronic health records to …
Hypothesis engineering for zero-shot hate speech detection
J Goldzycher, G Schneider - arXiv preprint arXiv:2210.00910, 2022 - arxiv.org
Standard approaches to hate speech detection rely on sufficient available hate speech
annotations. Extending previous work that repurposes natural language inference (NLI) …
annotations. Extending previous work that repurposes natural language inference (NLI) …
On the effectiveness of sentence encoding for intent detection meta-learning
Recent studies on few-shot intent detection have attempted to formulate the task as a meta-
learning problem, where a meta-learning model is trained with a certain capability to quickly …
learning problem, where a meta-learning model is trained with a certain capability to quickly …