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 safety filter, designed based on the CBF, to the output generation of the baseline LLM, i.e., the sequence of the token, with the aim of intervening in the generated text. The overall text-generation system is implemented with Llama 3 and a RoBERTa model, and the source code is available at https://github.com/Mya-Mya/CBF-LLM. The experiment demonstrates its control ability and effectiveness in reducing the number of interventions needed for user-specified alignment tasks.
@article{arxiv.2408.15625,
title = {CBF-LLM: Safe Control for LLM Alignment},
author = {Yuya Miyaoka and Masaki Inoue},
journal= {arXiv preprint arXiv:2408.15625},
year = {2026}
}