Large language models (LLMs) have shown powerful performance and development
prospect and are widely deployed in the real world. However, LLMs can capture social …

Despite being responsible for state-of-the-art results in several computer vision and natural
language processing tasks, neural networks have faced harsh criticism due to some of their …

Abstract Concept erasure aims to remove specified features from a representation. It can
improve fairness (eg preventing a classifier from using gender or race) and interpretability …

Gender is widely discussed in the context of language tasks and when examining the
stereotypes propagated by language models. However, current discussions primarily treat …

Deep neural networks are hegemonic approaches to many machine learning areas,
including natural language processing (NLP). Thanks to the availability of large corpora …

Modern neural models trained on textual data rely on pre-trained representations that
emerge without direct supervision. As these representations are increasingly being used in …

As the reach of large language models (LMs) expands globally, their ability to cater to
diverse cultural contexts becomes crucial. Despite advancements in multilingual …

Recent studies show that Natural Language Processing (NLP) technologies propagate
societal biases about demographic groups associated with attributes such as gender, race …

Large language models (LLMs) have strong capabilities in solving diverse natural language
processing tasks. However, the safety and security issues of LLM systems have become the …

Content Warning: This paper contains examples of misgendering and erasure that could be
offensive and potentially triggering. Gender bias in language technologies has been widely …