When Persona Attributes Improve Population Alignment in Large Language Models
Large Language Models (LLMs) are increasingly used to predict the responses of human participants in survey panels. Towards that goal, persona prompting has recently emerged as a technique to inform and align large pretrained language models. Persona prompting refers to the practice of using short textual descriptions of 'personas' in prompts to steer the LLM's generations. Personas describe individuals through different attributes such as their socio-demographics, attitudes, or behaviors, with the aim of aligning LLMs to produce responses that correlate with the corresponding human responses.
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- PossiblePossibly related (embedding) · 54%Understanding large language models demands distinguishing human projection from machine cognition - Nature →
- PossiblePossibly related (embedding) · 51%The Large Language Model (LLM) understands the world through human texts. A world model is needed f.. - 매일경제 →
- PossiblePossibly related (embedding) · 53%Persona-prompted LLM agents achieve modest but genuine prediction of human social media reactions - Nature →
- LinkedLinked via arxiv author · 85%Leon Fröhling →
“When Persona Attributes Improve Population Alignment in Large Language Models”
- LinkedLinked via arxiv author · 85%Jens Rupprecht →
“When Persona Attributes Improve Population Alignment in Large Language Models”
- LinkedLinked via arxiv author · 85%Markus Strohmaier →
“When Persona Attributes Improve Population Alignment in Large Language Models”
- LinkedLinked via arxiv author · 85%Claudia Wagner →
“When Persona Attributes Improve Population Alignment in Large Language Models”