Frasheri, Kris2025-01-022025-01-022025-01-022024-12-19https://hdl.handle.net/10012/21298The domain of machine learning (ML) has grappled with the challenge of curating subjective datasets, where there can be many equally valid labels due to differences in perspectives and a significant technical gap remains in how we can effectively incorporate multiple subjective viewpoints into the labelling process. We contribute PERSONA, a dataset labelling tool that presents LLM-generated personas with diverse labelling per- spectives to encourage annotators to consider different human values during the dataset labelling process. We studied how interactions with these personas affect the annotator’s decision-making patterns. Based on a two-part user study, our evaluation shows that PERSONA enriches the labelling process by prompting the annotators to reflect on differ- ent viewpoints, showing the potential value of integrating LLMs in machine learning data generation pipelines.enMaster Thesis