3 accepted papers
As Large Language Models (LLMs) increasingly integrate into everyday workflows, where users shape outcomes through multi-turn collaboration, a critical question emerges: do users with different personality traits systematically prefer certain LLMs over others? We conduc-ted a study with 32 participa
This study explores the effectiveness of Large Language Models (LLMs) in creating personalized “mirror stories” that reflect and resonate with individual readers’ identities, addressing the significant lack of diversity in literature. We present MirrorStories, a corpus of 1,500 personalized short st…
Research on Large Language Models (LLMs) has often neglected subtle biases that, although less apparent, can significantly influence the models’ outputs toward particular social narratives. This study addresses two such biases within LLMs: representative bias, which denotes a tendency of LLMs to gen…