CBF-LLM: Safe Control for LLM Alignment
This addresses safety and alignment issues in LLMs for users and developers, but it appears incremental as it builds on existing control methods applied to a new domain.
The paper tackles the problem of aligning large language models (LLMs) with user preferences by proposing a control-based framework using a control barrier function (CBF) to filter generated text, resulting in reduced interventions for alignment tasks.
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.