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

Large Language Models (LLMs) have demonstrated remarkable success across various
domains. However, despite their promising performance in numerous real-world …

Rapid advancements of large language models (LLMs) have enabled the processing,
understanding, and generation of human-like text, with increasing integration into systems …

In the fast-evolving domain of artificial intelligence, large language models (LLMs) such as
GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law …

Large Language Models (LLMs) can generate biased responses. Yet previous direct
probing techniques contain either gender mentions or predefined gender stereotypes, which …

Gender inequality is embedded in our communication practices and perpetuated in
translation technologies. This becomes particularly apparent when translating into …

Abstract Pre-trained Language Models (PLMs) have been shown to inherit and even amplify
the social biases contained in the training corpus, leading to undesired stereotype in real …

Gender-neutral translation (GNT) that avoids biased and undue binary assumptions is a
pivotal challenge for the creation of more inclusive translation technologies. Advancements …

As part of the WMT-2023" Test suites" shared task, in this paper we summarize the results of
two test suites evaluations: MuST-SHE-WMT23 and INES. By focusing on the en-de and de …

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 …