arXiv AI By Jinhee Won, Xinlan Emily Hu

Personas Differ from Native-Language Generation: Language Pathways Shape LLM Interpersonal Advice

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arXiv Computation and Language
Sep 3

When Persona Attributes Improve Population Alignment in Large Language Models

The paper investigates how persona prompting—using short textual descriptions of individuals—to align large language models (LLMs) with human survey responses. It examines the impact of selecting different persona attributes and finds that not all attribute combinations improve performance, suggesting that the variation in human responses to survey questions may explain mixed results. The study evaluates multiple attribute selection methods across four social surveys, two countries, six LLMs, and twenty prediction tasks, offering guidance on when persona prompting is beneficial and which attribute choices are most effective.

By Leon Fr\"ohling, Jens Rupprecht, Markus Strohmaier, Claudia Wagner
arXiv Computation and Language
Sep 7

Controlling and Assessing Appropriate Persona Use in LLM-based Dialogue Generation

The paper examines how large language models (LLMs) tend to overuse persona attributes in persona-based dialogue generation, producing unnatural responses. It identifies a systematic bias in LLMs to incorporate all provided persona details and shows that current metrics cannot assess contextual appropriateness. To address this, the authors introduce Self-CONtrastive Persona Overuse Suppression (SCONPOS), which intervenes in the prompt encoding stage to reduce overuse, and propose the Persona Appropriateness Score (PAS), a new metric that penalizes both overuse and underuse of persona attributes.

By Jongkyung Shin, Inkyu Lee, Chiehyeon Lim