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    <abstract>We present a novel approach for enhancing diversity and control in data annotation tasks by personalizing large language models (LLMs). We investigate the impact of injecting diverse persona descriptions into LLM prompts across two studies, exploring whether personas increase annotation diversity and whether the impacts of individual personas on the resulting annotations are consistent and controllable. Our results indicate that persona-prompted LLMs generate more diverse annotations than LLMs prompted without personas, and that the effects of personas on LLM annotations align with subjective differences in human annotations. These effects are both controllable and repeatable, making our approach a valuable tool for enhancing data annotation in subjective NLP tasks such as toxicity detection.</abstract>
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%0 Conference Proceedings
%T Personas with Attitudes: Controlling LLMs for Diverse Data Annotation
%A Fröhling, Leon
%A Demartini, Gianluca
%A Assenmacher, Dennis
%Y Calabrese, Agostina
%Y de Kock, Christine
%Y Nozza, Debora
%Y Plaza-del-Arco, Flor Miriam
%Y Talat, Zeerak
%Y Vargas, Francielle
%S Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-105-6
%F frohling-etal-2025-personas
%X We present a novel approach for enhancing diversity and control in data annotation tasks by personalizing large language models (LLMs). We investigate the impact of injecting diverse persona descriptions into LLM prompts across two studies, exploring whether personas increase annotation diversity and whether the impacts of individual personas on the resulting annotations are consistent and controllable. Our results indicate that persona-prompted LLMs generate more diverse annotations than LLMs prompted without personas, and that the effects of personas on LLM annotations align with subjective differences in human annotations. These effects are both controllable and repeatable, making our approach a valuable tool for enhancing data annotation in subjective NLP tasks such as toxicity detection.
%U https://aclanthology.org/2025.woah-1.43/
%P 468-481

Markdown (Informal)

[Personas with Attitudes: Controlling LLMs for Diverse Data Annotation](https://aclanthology.org/2025.woah-1.43/) (Fröhling et al., WOAH 2025)

• Personas with Attitudes: Controlling LLMs for Diverse Data Annotation (Fröhling et al., WOAH 2025)

ACL

• Leon Fröhling, Gianluca Demartini, and Dennis Assenmacher. 2025. Personas with Attitudes: Controlling LLMs for Diverse Data Annotation. In Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH), pages 468–481, Vienna, Austria. Association for Computational Linguistics.