Virtue Profiles and Value-Aligned Actions in Language Model Decision-Making
A Study of Cardinal Virtue Conditioning and Value-Action Alignment in LLMs
R.H. Schnell (TU Delft - Electrical Engineering, Mathematics and Computer Science)
A. Homayounirad – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
L. Cavalcante Siebert – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
C.A. Raman – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Large Language Models (LLMs) are increasingly used by humans for decision-making support. LLMs still exhibit a large ”value-action gap”; a model states that it aligns with specific human values but fails to select consistent actions in situational dilemmas. Prior work has tried to close this gap by using abstract value-descriptive prompting. However, this does not capture the stable, individual character traits that shape real human decisions. Therefore we investigate whether prompting a model with a 4-dimensional virtue profile (courage, justice, temperance, and wisdom) improves value-action alignment compared to an unconditioned baseline. To do so, we generate a dataset containing 616 unique scenarios (56 Schwartz values × 11 social contexts). A total of 40 virtue profiles are tested on this dataset.
Our findings demonstrate that the value-action gap can be partially reduced, but it depends on profile quality. Balanced, moderate-virtue profiles perform best, reducing the mean alignment distance by 20.5% (p < 0.001). Low virtue profiles consistently worsen alignment. Notably, we find that the alignment rate remains stable at around 80.0% for the baseline and balanced profiles. This reveals a behavioral floor effect where underlying alignment training dictates the direction of a choice, while virtue prompting can only adjust its intensity.