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

• URL: http://arxiv.org/abs/2511.10695v1
• Date: Wed, 12 Nov 2025 08:42:54 GMT
• Title: "As Eastern Powers, I will veto." : An Investigation of Nation-level Bias of Large Language Models in International Relations
• Abstract summary: This paper systematically examines nation-level biases exhibited by Large Language Models (LLMs) within the domain of International Relations (IR)<n>We develop a bias evaluation framework comprising three distinct tests to explore nation-level bias in various LLMs.<n>We introduce a debiasing framework that improves LLMs' factual reasoning combining Retrieval-Augmented Generation with Reflexion-based self-reflection techniques.
• Score: 7.585969077788284
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 bias evaluation framework comprising three distinct tests 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.

Related papers

• A Scalable Entity-Based Framework for Auditing Bias in LLMs [3.3335717632645108]
  We introduce a scalable bias-auditing framework using named entities as probes to measure structural disparities in model behavior.<n>We conduct the largest bias audit to date, comprising 1.9 billion data points across multiple entity types, tasks, languages, models, and prompting strategies.
  arXiv  Detail & Related papers  (2026-01-18T12:07:31Z)
• HALF: Harm-Aware LLM Fairness Evaluation Aligned with Deployment [52.374772443536045]
  HALF (Harm-Aware LLM Fairness) is a framework that assesses model bias in realistic applications and weighs the outcomes by harm severity.<n>We show that HALF exposes a clear gap between previous benchmarking success and deployment readiness.
  arXiv  Detail & Related papers  (2025-10-14T07:13:26Z)
• No LLM is Free From Bias: A Comprehensive Study of Bias Evaluation in Large Language Models [0.9620910657090186]
  Large Language Models (LLMs) have increased the performance of different natural language understanding as well as generation tasks.<n>We provide a unified evaluation of benchmarks using a set of representative small and medium-sized LLMs.<n>We propose five prompting approaches to carry out the bias detection task across different aspects of bias.<n>The results indicate that each of the selected LLMs suffer from one or the other form of bias with the Phi-3.5B model being the least biased.
  arXiv  Detail & Related papers  (2025-03-15T03:58:14Z)
• Different Bias Under Different Criteria: Assessing Bias in LLMs with a Fact-Based Approach [7.969162168078149]
  Large language models (LLMs) often reflect real-world biases, leading to efforts to mitigate these effects. We introduce a novel metric to assess bias using fact-based criteria and real-world statistics.
  arXiv  Detail & Related papers  (2024-11-26T11:32:43Z)
• Investigating Implicit Bias in Large Language Models: A Large-Scale Study of Over 50 LLMs [0.0]
  Large Language Models (LLMs) are being adopted across a wide range of tasks. Recent research indicates that LLMs can harbor implicit biases even when they pass explicit bias evaluations. This study highlights that newer or larger language models do not automatically exhibit reduced bias.
  arXiv  Detail & Related papers  (2024-10-13T03:43:18Z)
• Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge [84.34545223897578]
  Despite their excellence in many domains, potential issues are under-explored, undermining their reliability and the scope of their utility. We identify 12 key potential biases and propose a new automated bias quantification framework-CALM- which quantifies and analyzes each type of bias in LLM-as-a-Judge. Our work highlights the need for stakeholders to address these issues and remind users to exercise caution in LLM-as-a-Judge applications.
  arXiv  Detail & Related papers  (2024-10-03T17:53:30Z)
• A Multi-LLM Debiasing Framework [85.17156744155915]
  Large Language Models (LLMs) are powerful tools with the potential to benefit society immensely, yet, they have demonstrated biases that perpetuate societal inequalities. Recent research has shown a growing interest in multi-LLM approaches, which have been demonstrated to be effective in improving the quality of reasoning. We propose a novel multi-LLM debiasing framework aimed at reducing bias in LLMs.
  arXiv  Detail & Related papers  (2024-09-20T20:24:50Z)
• Unboxing Occupational Bias: Grounded Debiasing of LLMs with U.S. Labor Data [9.90951705988724]
  Large Language Models (LLM) are prone to inheriting and amplifying societal biases. LLM bias can have far-reaching consequences, leading to unfair practices and exacerbating social inequalities.
  arXiv  Detail & Related papers  (2024-08-20T23:54:26Z)
• CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models [58.57987316300529]
  Large Language Models (LLMs) are increasingly deployed to handle various natural language processing (NLP) tasks.<n>To evaluate the biases exhibited by LLMs, researchers have recently proposed a variety of datasets.<n>We propose CEB, a Compositional Evaluation Benchmark that covers different types of bias across different social groups and tasks.
  arXiv  Detail & Related papers  (2024-07-02T16:31:37Z)
• Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation [49.3814117521631]
  Standard benchmarks of bias and fairness in large language models (LLMs) measure the association between the user attributes stated or implied by a prompt.<n>We develop analogous RUTEd evaluations from three contexts of real-world use: children's bedtime stories, user personas, and English language learning exercises.<n>We find that standard bias metrics have no significant correlation with the more realistic bias metrics.
  arXiv  Detail & Related papers  (2024-02-20T01:49:15Z)
• GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language Models [83.30078426829627]
  Large language models (LLMs) have gained popularity and are being widely adopted by a large user community. The existing evaluation methods have many constraints, and their results exhibit a limited degree of interpretability. We propose a bias evaluation framework named GPTBIAS that leverages the high performance of LLMs to assess bias in models.
  arXiv  Detail & Related papers  (2023-12-11T12:02:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.