4 citations · 14 across the 6 of their papers we have counts for
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cs.CL2024
From 'Showgirls' to 'Performers': Fine-tuning with Gender-inclusive Language for Bias Reduction in LLMs
Marion Bartl, Susan Leavy
Gender bias is not only prevalent in Large Language Models (LLMs) and their training data, but also firmly ingrained into the structural aspects of language itself. Therefore, adap…
cs.CL2023★ 2 cited
Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning
Susan Leavy, Emilie Pine, Mark T Keane
We present a text mining system to support the exploration of large volumes of text detailing the findings of government inquiries. Despite their historical significance and potent…
cs.CL2022★ 4 cited
Towards Lexical Gender Inference: A Scalable Methodology using Online Databases
Marion Bartl, Susan Leavy
This paper presents a new method for automatically detecting words with lexical gender in large-scale language datasets. Currently, the evaluation of gender bias in natural languag…