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

Recently, large language models (LLMs) have attracted considerable attention due to their
remarkable capabilities. However, LLMs' generation of biased or hallucinatory content …

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 …

Large Language Models (LLMs) are trained primarily on minimally processed web text,
which exhibits the same wide range of social biases held by the humans who created that …

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

Large language models (LLMs) have shown remarkable advances in language generation
and understanding but are also prone to exhibiting harmful social biases. While recognition …

Natural language generation models reproduce and often amplify the biases present in their
training data. Previous research explored using sequence-to-sequence rewriting models to …

Gender bias is largely recognized as a problematic phenomenon affecting language
technologies, with recent studies underscoring that it might surface differently across …

Gender inclusivity in language technologies has become a prominent research topic. In this
study, we explore gender-neutral translation (GNT) as a form of gender inclusivity and a goal …