Introduces a gradient-based multilingual audit framework for LLM moral decisions in robot assistance scenarios and reports persistent culturally asymmetric gradient tracking failures not fixed by prompting.
Ethical Reasoning and Moral Value Alignment of LLMs Depend on the Language We Prompt Them In
3 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
3
Pith papers citing it
7
external citations · external index
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Politeness in prompts boosts average LLM response quality by up to 11% but the benefit is language- and model-dependent, with English favoring courteous tones, Hindi deferential ones, and Spanish assertive ones.
Reanalyzing MoReBench by assigning LLMs the task of generating scoring rubrics shows better calibration to human rubrics and suggests stronger LLM moral reasoning than previously reported.
citing papers explorer
-
Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients
Introduces a gradient-based multilingual audit framework for LLM moral decisions in robot assistance scenarios and reports persistent culturally asymmetric gradient tracking failures not fixed by prompting.
-
No Universal Courtesy: A Cross-Linguistic, Multi-Model Study of Politeness Effects on LLMs Using the PLUM Corpus
Politeness in prompts boosts average LLM response quality by up to 11% but the benefit is language- and model-dependent, with English favoring courteous tones, Hindi deferential ones, and Spanish assertive ones.
-
Are LLMs Bad at Moral Reasoning?
Reanalyzing MoReBench by assigning LLMs the task of generating scoring rubrics shows better calibration to human rubrics and suggests stronger LLM moral reasoning than previously reported.