CBF-LLM: Safe Control for LLM Alignment
Keywords: LLM, alignment, control barrier function
TL;DR: Research addressing LLM alignment from a control theory approach.
Abstract: 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 Llama 3 and a BERT model, aiming to generate positive text.
Finally, further applications and limitations of the CBF-LLM for other alignment tasks, including topic-keeping and hallucination mitigating, are discussed.
Supplementary Material: zip
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 8657
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