Year: 2025
Type: conference-paper
Abstract: Large Language Models (LLMs) need to adapt their predictions to diverse cultural contexts to benefit diverse communities across the world.While previous efforts have focused on single-LLM, single-turn approaches, we propose to exploit the complementary strengths of multiple LLMs to promote cultural adaptability.We introduce a Multi-Agent Debate framework, where two LLM-based agents debate over a cultural scenario and collaboratively reach a final decision.We propose two variants: one where either LLM agents exclusively debate and another where they dynamically choose between self-reflection and debate during their turns.We evaluate these approaches on 7 openweight LLMs (and 21 LLM combinations) using the NORMAD-ETI benchmark for social etiquette norms in 75 countries.Experiments show that debate improves both overall accuracy and cultural group parity over single-LLM baselines.Notably, multi-agent debate enables relatively small LLMs (7-9B) to achieve accuracies comparable to that of a much larger model (27B parameters).
Institution: University of Maryland, College Park
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4412945147