25 citations · 47 across the 10 of their papers we have counts for
10 papers
Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societies
Vithya Yogarajan, Gillian Dobbie, Te Taka Keegan +1
The benefits and capabilities of pre-trained language models (LLMs) in current and future innovations are vital to any society. However, introducing and using LLMs comes with biase…
Challenges in Annotating Datasets to Quantify Bias in Under-represented Society
Vithya Yogarajan, Gillian Dobbie, Timothy Pistotti +2
Recent advances in artificial intelligence, including the development of highly sophisticated large language models (LLM), have proven beneficial in many real-world applications. H…
Neuromodulation Gated Transformer
Kobe Knowles, Joshua Bensemann, Diana Benavides-Prado +4
We introduce a novel architecture, the Neuromodulation Gated Transformer (NGT), which is a simple implementation of neuromodulation in transformers via a multiplicative effect. We…
Effectiveness of Debiasing Techniques: An Indigenous Qualitative Analysis
Vithya Yogarajan, Gillian Dobbie, Henry Gouk
An indigenous perspective on the effectiveness of debiasing techniques for pre-trained language models (PLMs) is presented in this paper. The current techniques used to measure and…
The Development of a Labelled te reo Māori-English Bilingual Database for Language Technology
Jesin James, Isabella Shields, Vithya Yogarajan +4
Te reo Māori (referred to as Māori), New Zealand's indigenous language, is under-resourced in language technology. Māori speakers are bilingual, where Māori is code-switched with E…
Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents
Vithya Yogarajan, Bernhard Pfahringer, Tony Smith +1
Multi-label learning predicts a subset of labels from a given label set for an unseen instance while considering label correlations. A known challenge with multi-label classificati…