道德建议作为互动协商：大语言模型回应中的框架、用户压力与社会位置

Computer Science > Computers and Society arXiv:2609.05345 (cs) [Submitted on 4 Sep 2026] Title:Moral Advice as Interactional Negotiation: Framing, User Pressure, and Social Position in Large Language Model Responses Authors:Minne Chen, Yourong Yao View a PDF of the paper titled Moral Advice as Interactional Negotiation: Framing, User Pressure, and Social Position in Large Language Model Responses, by Minne Chen and Yourong Yao View PDF Abstract:As conversational AI becomes a source of everyday guidance, LLMs increasingly participate in the interpretation and legitimation of morally contested choices. We examine LLM moral advice as an interactional negotiation shaped by framing, sustained user pressure, and the moral subject's social position. Using GPT-4o-mini as an illustrative case, we conducted a factorial vignette experiment with a pre-specified three-round protocol. The model received eldercare dilemmas that varied in framing and persona, followed by two user challenges. We analyzed 1,620 configuration-framing cells, each repeated three times, yielding 4,860 conversational runs. Caregiving affirmation produced near-uniform endorsement, whereas non-caregiving framing produced more variable baseline stances. When users challenged caregiving endorsement, 90.1% of configurations shifted after one round. Non-caregiving framing produced more resistant and unstable trajectories. Never (27.9%) and Late (25.6%) accommodations were more common than Early accommodations (16.5%), and only 14.32% of configurations achieved perfect trajectory consistency, compared with 62.72% under caregiving framing. Advice also varied with social position. Female personas received more support for non-caregiving decisions, while the presence of sisters increased accommodation. The GPT-4o-mini case shows that LLM moral advice can develop through a partially stable negotiation between normative response tendencies and user pressure rather than express a fixed ethical framework. The framework and design support comparative research across models and moral domains. Such instability raises social, ethical, and technical concerns, as users may treat advice that is difficult to scrutinize as objective. Subjects: Computers and Society (cs.CY) Cite as: arXiv:2609.05345 [cs.CY] (or arXiv:2609.05345v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2609.05345 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Minne Chen [view email] [v1] Fri, 4 Sep 2026 16:48:33 UTC (984 KB) Full-text links: Access Paper: View a PDF of the paper titled Moral Advice as Interactional Negotiation: Framing, User Pressure, and Social Position in Large Language Model Responses, by Minne Chen and Yourong Yao View PDF view license Current browse context: cs.CY new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)