“As Eastern Powers, I will veto.” : An Investigation of Nation-level Bias of Large Language Models in International Relations

ACL ARR 2025 May Submission3208 Authors

Abstract: This paper systematically examines nation-level biases exhibited by Large Language Models (LLMs) within the domain of international relations (IR). Leveraging historical records from the United Nations Security Council (UNSC), we developed a multi-faceted bias evaluation framework to explore nation-level bias in various LLMs, with a particular focus on the five permanent members of the UNSC. Experimental results show that, even with the general bias patterns across models (e.g., favorable biases toward the western nations, and unfavorable biases toward Russia), these still vary based on the LLM. Notably, even within the same LLM, the direction and magnitude of bias for a nation change depending on the evaluation context. This observation suggests that LLM biases are fundamentally multidimensional, varying across models and tasks. We also observe that models with stronger reasoning abilities show reduced bias and better performance. building on this finding, we introduce a debiasing framework that improves LLMs’ factual reasoning combining Retrieval-Augmented Generation with Reflexion-based self-reflection techniques. Experiments show it effectively reduces nation-level bias, and improves performance, particularly in GPT-4o-mini and LLama-3.3-70B. Our findings emphasize the need to assess nation-level bias alongside performance when applying LLMs in the IR domain.

Paper Type: Long

Research Area: Ethics, Bias, and Fairness

Research Area Keywords: model bias/fairness evaluation, model bias/unfairness mitigation, international relations applications, evaluation methodologies,

Contribution Types: Model analysis & interpretability, Data resources

Languages Studied: english

Submission Number: 3208