infosense/frameref-personas

FrameRef Persona Adapters

This repository contains the persona adapter models presented in the paper FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Health.

Model Information

All adapters in this repository are LoRA adapters trained on top of the Llama-3.1-8B-Instruct base model using 15k training samples. For each framing dimension (authoritative, consensus, emotional, prestige, and sensationalist), we release three independently trained adapters. The baseline adapters were trained with alpha = 1, while all other adapters were trained with alpha = 0.3.

Within the FrameRef framework, these framing-sensitive agent personas are constructed by fine-tuning language models with framing-conditioned loss attenuation, inducing targeted biases while preserving overall task competence. For full details, see the accompanying paper.

Resources

• Code Repository: GitHub - infosenselab/frameref
• Dataset: infosense/frameref
• Paper: arXiv:2602.15273

Citing FrameRef

If you use this resource in your projects, please cite the following paper:

@misc{De_Lima_FrameRef_A_Framing_2025,
author = {De Lima, Victor and Liu, Jiqun and Yang, Grace Hui},
doi = {10.48550/arXiv.2602.15273},
title = {{FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Health}},
url = {https://arxiv.org/abs/2602.15273},
year = {2025}
}