MedConstBench: Benchmarking How Constitutions Steer LLMs in Medical Ethics

Authors: Yibo Yang , Zerui Huang , Haomiao Yang , Zixia Wang

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Abstract: As large language models advance rapidly, they are increasingly used to answer medical questions, and many such models are now deployed locally. Once a model is deployed, its behavior is governed mainly by the ethical policy written into its constitution. However, most existing work focuses on factual correctness rather than the trade-off between competing ethical values. Even benchmarks that do evaluate ethics treat it as a fixed property of the model rather than as a variable, so how a constitution reshapes medical-ethics behavior remains largely unexplored. We introduce MedConstBench, a medical-ethics benchmark that treats the deployment constitution as a controlled variable, is organized around Beauchamp's four principles, and is scored by human-calibrated multi-model peer review. It includes MedDilemma, our own dataset of clinical dilemmas, and supports a systematic analysis of how constitutions steer LLMs. Evaluating ten open-source LLMs under four constitutions, we find that the effect of a constitution is dimension-selective and model-dependent, and that no constitution is universally optimal. Motivated by these findings, we propose a do-no-harm adaptive selector that chooses a constitution per model, matching the per-model oracle while guaranteeing a worst-case floor and generalizing to unseen models at low cost.

Comments:

8 pages, 3 figures, 8 tables

Subjects:

Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL)

Cite as:

arXiv:2610.36513 [cs.AI]

(or

arXiv:2610.36513v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.36513

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yang Yibo [ view email ]

[v1]

Tue, 27 Oct 2026 02:17:13 UTC (2,401 KB)

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