---
language:
- en
license: cc-by-sa-3.0
task_categories:
- text-generation
---

## Dataset Information

AI systems increasingly shape how individuals access and evaluate information, intensifying concerns about misinformation. Computational simulation offers a controlled complement to direct empirical study, with fine-tuned LLMs enabling powerful agent-based simulations. However, fine-tuning often induces broad behavioral shifts including bias drift, degenerate heuristics, and changes in baseline task behavior, which undermine experimental validity. The central challenge is inducing localized decision shifts while preserving the model's aggregate behavior outside the targeted intervention. To study these effects in a controlled setting, we introduce *FrameRef*, a large-scale dataset of semantically equivalent claims with controlled surface framings across five dimensions (Authoritative, Consensus, Emotional, Sensationalist, Prestige).  For full details, see the accompanying paper [here](https://doi.org/10.48550/arXiv.2602.15273).


## Resources

- **Code Repository**: [GitHub - infosenselab/frameref](https://github.com/infosenselab/framing-sensitive-ft)
- **Persona Adapters**: [infosense/frameref](https://huggingface.co/infosense/frameref-personas)
- **Paper**: [arXiv](https://arxiv.org/abs/2602.15273), [Hugging Face](https://huggingface.co/papers/2602.15273)




## Citing this work.

Please cite the following paper when referencing this work.

```bibtex
@misc{de_lima_stability_2026,
author = {De Lima, Victor and Liu, Jiqun and Yang, Grace Hui},
doi = {10.48550/arXiv.2602.15273},
title = {Stability {Regimes} for {Framing}-{Sensitive} {Fine}-{Tuning} in {Language} {Models}},
url = {https://doi.org/10.48550/arXiv.2602.15273},
year = {2026}
}
```