This is the repo for Fints: Efficient Inference-Time Personalization for LLMs with Fine-Grained Instance-Tailored Steering.
See under [data_process/]. The preparation process is for Headline Generation, Abstract Writing, and PersonalWAB
Run
./new_data_process.py
to select some users for experiments. Run
./ranking.py --task [your_task]
to sort all historical samples according to their relevance to the corresponding training or testing samples.
For PersonalWAB, run
python data_collect_pwab.py --dataset pwab \
--modelweight [root_of_models] \
--k 5 \
--llm llama-3.1 \
--form json \
--data_path ../pa_back/data
to generate positive samples.
For all datasets, run
./data_collect.sh
to generate negtive samples.
Run
./run.sh
to generate personalized steering vectors and evaluate the model on the test set.
After evaluation, for PersonalWAB, the evaluation scores will be saved; for other datasets, add the parameter --eval in run_generation.py and run again to obtain the model's evaluation scores.
By adding --plugin in run_generation.py, PA-steering can be used with lora models.
If you find this repo useful, please cite our paper:
@misc{du2025fintsefficientinferencetimepersonalization,
title={Fints: Efficient Inference-Time Personalization for LLMs with Fine-Grained Instance-Tailored Steering},
author={Kounianhua Du and Jianxing Liu and Kangning Zhang and Wenxiang Jiao and Yuan Lu and Jiarui Jin and Weiwen Liu and Yong Yu and Weinan Zhang},
year={2025},
eprint={2510.27206},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2510.27206},
}