我该去哪里学习？有偏见的语言模型说了算！评估学术推荐语言模型的公平性
Where Should I Study? Biased Language Models Decide! Evaluating Fairness in LMs for Academic Recommendations
AI总结：
本文实证考察LLaMA-3.1-8B等3个开源LLM在学术推荐中的地理、人口和经济偏见，提出多维评估框架，发现其存在系统性偏差，强调需解决偏见以保障高等教育公平获取。
摘要：
大型语言模型（LLMs）正越来越多地被用作教育规划等任务的日常推荐系统，但其推荐存在延续社会偏见的风险。本文通过实证研究，考察了三个开源LLM（LLaMA-3.1-8B、Gemma-7B和Mistral-7B）在大学和项目推荐中的地理、人口和经济偏见。我们使用了360个按性别、国籍和经济状况变化的模拟用户档案，分析了超过25000条推荐。结果显示出强烈的偏见：全球北方的机构受到不成比例的青睐，推荐往往强化性别刻板印象，且机构重复现象普遍。尽管LLaMA-3.1实现了最高的多样性，推荐了58个国家的481所独特大学，但系统性差异仍然存在。为了量化这些问题，我们提出了一个新颖的多维评估框架，该框架超越了准确性，通过测量人口和地理代表性来评估。我们的研究结果强调，在教育领域的LLM中考虑偏见以确保全球公平获得高等教育的迫切性。
英文摘要：
Large Language Models (LLMs) are increasingly used as daily recommendation systems for tasks like education planning, yet their recommendations risk perpetuating societal biases. This paper empirically examines geographic, demographic, and economic biases in university and program suggestions from three open-source LLMs: LLaMA-3.1-8B, Gemma-7B, and Mistral-7B. Using 360 simulated user profiles varying by gender, nationality, and economic status, we analyze over 25,000 recommendations. Results show strong biases: institutions in the Global North are disproportionately favored, recommendations often reinforce gender stereotypes, and institutional repetition is prevalent. While LLaMA-3.1 achieves the highest diversity, recommending 481 unique universities across 58 countries, systemic disparities persist. To quantify these issues, we propose a novel, multi-dimensional evaluation framework that goes beyond accuracy by measuring demographic and geographic representation. Our findings highlight the urgent need for bias consideration in educational LMs to ensure equitable global access to higher education.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
