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

Automatic summarization of legal case judgements has traditionally been attempted by
using extractive summarization methods. However, in recent years, abstractive …

Deep neural networks often learn unintended bias during training, which might have harmful
effects when deployed in realworld settings. This work surveys 214 papers related to …

The maturity level of language models is now at a stage in which many companies rely on
them to solve various tasks. However, while research has shown how biased and harmful …

Taking an interdisciplinary approach to surveying issues around gender bias in textual and
visual AI, we present literature on gender bias detection and mitigation in NLP, CV, as well …

With language models becoming increasingly ubiquitous, it has become essential to
address their inequitable treatment of diverse demographic groups and factors. Most …

The societal impact of pre-trained language models has prompted researchers to probe
them for strong associations between protected attributes and value-loaded terms, from slur …

The growing capability and availability of generative language models has enabled a wide
range of new downstream tasks. Academic research has identified, quantified and mitigated …

Generated texts from large pretrained language models have been shown to exhibit a
variety of harmful, human-like biases about various demographics. These findings prompted …

Considerable efforts to measure and mitigate gender bias in recent years have led to the
introduction of an abundance of tasks, datasets, and metrics used in this vein. In this position …