This paper proposes a control-based framework for aligning large language models (LLMs) by leveraging a control barrier function (CBF) to ensure user-desirable text generation. The presented framework applies the CBF safety filter to the predicted token generated from the baseline LLM, to intervene in the generated text. The safety filter includes two significant advantages: this safety filter is an add-on type, allowing it to be used for alignment purposes without fine-tuning the baseline LLM, and if there is an evaluation model regarding the desired alignment, it can be directly applied to the filter design. The overall text-generation system is implemented with open-source language models, aiming to generate positive text.
@article{arxiv.2511.03121,
title = {Control Barrier Function for Aligning Large Language Models},
author = {Yuya Miyaoka and Masaki Inoue},
journal= {arXiv preprint arXiv:2511.03121},
year = {2026}
}
Comments
This work is an extenede version of arXiv:2408.15625 and has been submitted to the IEEE for possible publication