LLMs Homogenize Values in Constructive Arguments on Value-Laden Topics
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Abstract (arXiv)
Large language models (LLMs) are increasingly used to promote prosocial and constructive discourse online. Yet little is known about how these models negotiate and shape underlying values when reframing people's arguments on value-laden topics. We conducted experiments with 465 participants from India and the United States, who wrote comments on homophobic and Islamophobic threads, and reviewed human-written and LLM-rewritten constructive versions of these comments. Our analysis shows that LLM systematically diminishes Conservative values while elevating prosocial values such as Benevolence and Universalism. When these comments were read by others, participants opposing same-sex marriage or Islam found human-written comments more aligned with their values, whereas those supportive of these communities found LLM-rewritten versions more aligned with their values. These findings suggest that value homogenization in LLM-mediated prosocial discourse runs the risk of marginalizing conservative viewpoints on value-laden topics and may inadvertently shape the dynamics of online discourse.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Sep 2025.
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Citing papers per year: 2026: 3.
3 papers.
- 2026Growth First, Care Second? Tracing the Landscape of LLM Value Preferences in Everyday Dilemmascited by 1
- 2026PLURAL: A Global Dataset for Value Alignmentcited by 1
- 2026Deflecting the Value Compass: Interacting with Large Language Models Temporarily Shifts Human Value Priorities Toward Personal Focuscited by 0
- 2026Deflecting the Value Compass: Interacting with Large Language Models Temporarily Shifts Human Value Priorities Toward Personal Focus22 Sep 2026
- 2026PLURAL: A Global Dataset for Value Alignment9 Jul 2026
- 2026Growth First, Care Second? Tracing the Landscape of LLM Value Preferences in Everyday Dilemmas4 Feb 2026