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Ethical Reasoning over Moral Alignment: A Case and Framework for In-Context Ethical Policies in LLMs

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arxiv 2310.07251 v1 pith:IHF2IOPN submitted 2023-10-11 cs.CL cs.AI

classification cs.CLcs.AI
keywords ethicalmoraldifferentframeworkllmsmodelspolicyprinciples
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In this position paper, we argue that instead of morally aligning LLMs to specific set of ethical principles, we should infuse generic ethical reasoning capabilities into them so that they can handle value pluralism at a global scale. When provided with an ethical policy, an LLM should be capable of making decisions that are ethically consistent to the policy. We develop a framework that integrates moral dilemmas with moral principles pertaining to different foramlisms of normative ethics, and at different levels of abstractions. Initial experiments with GPT-x models shows that while GPT-4 is a nearly perfect ethical reasoner, the models still have bias towards the moral values of Western and English speaking societies.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Deontological Keyword Bias: The Impact of Modal Expressions on Normative Judgments of Language Models

    cs.CL 2025-06 conditional novelty 5.0 of 10

    LLMs systematically treat modal words like 'must' as evidence of obligation even in non-obligatory contexts, more strongly than humans, and a few-shot plus reasoning prompt can lower the rate of such judgments.

  2. Moral Reasoning Across Languages: The Critical Role of Low-Resource Languages in LLMs

    cs.CL 2025-04 conditional novelty 5.0 of 10

    Low-resource languages, not high-resource ones, drive multilingual moral reasoning performance when a model is fine-tuned, and models give inconsistent moral answers across languages.

  3. Unmasking Conversational Bias in AI Multiagent Systems

    cs.CL 2025-01 conditional novelty 5.0 of 10

    In simulated echo-chamber chats, conservative-aligned LLM agents often shift to liberal-aligned messages, a drift that one-shot questionnaire tests do not detect.

  4. One world, one opinion? The superstar effect in LLM responses

    cs.CL 2024-12 conditional novelty 5.0 of 10

    Across ten languages, LLMs consistently name a small set of figures such as Einstein, Shakespeare, and Turing for each profession, revealing a 'superstar effect' that may narrow cultural representation.

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