大城市偏见：评估都市规模对语言模型计算性就业市场能力的影响
Big City Bias: Evaluating the Impact of Metropolitan Size on Computational Job Market Abilities of Language Models
AI总结：
本研究量化大型语言模型在美国384个都市区域零样本就业预测中的都市规模偏见，发现较小区域性能显著更差，最小10个区域比最大10个区域性能差超300%。
摘要：
大型语言模型（LLMs）已成为求职者和雇主进行工作匹配的有用技术。工作匹配通常基于特定的地理位置，如城市或地区。然而，大型语言模型存在已知的偏见，这些偏见通常源自其训练数据。在本研究中，我们旨在量化大型语言模型中编码的都市规模偏见，评估美国384个都市区域在零样本薪资、雇主存在性和通勤时长预测方面的表现。在所有基准测试中，我们观察到都市规模与大型语言模型性能之间存在负相关，表明较小的区域确实代表性不足。更具体地说，最小的10个都市区域比最大的10个都市区域的基准测试性能差高达300%以上。
英文摘要：
Large language models (LLMs) have emerged as a useful technology for job matching, for both candidates and employers. Job matching is often based on a particular geographic location, such as a city or region. However, LLMs have known biases, commonly derived from their training data. In this work, we aim to quantify the metropolitan size bias encoded within large language models, evaluating zero-shot salary, employer presence, and commute duration predictions in 384 of the United States' metropolitan regions. Across all benchmarks, we observe negative correlations between the metropolitan size and the performance of the LLMS, indicating that smaller regions are indeed underrepresented. More concretely, the smallest 10 metropolitan regions show upwards of 300% worse benchmark performance than the largest 10.
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。
