Big City Bias: Evaluating the Impact of Metropolitan Size on Computational Job Market Abilities of Language Models (2024)

Abstract: Large language models 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, LMs 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 correlations between metropolitan population and the accuracy of predictions, with the smallest 10 metropolitan regions showing upwards of 300% worse benchmark performance than the largest 10.

## introduction recent large language models (llms) are primarily trained on internet-derived corpora @xcite . these underlying datasets are prone to linguistic and geographic bias. for example, the training data of common crawl, used to train openai's gpt-3 @xcite and meta ai's llama @xcite , is composed of 46% english language documents. such lexical imbalances contribute to an anglophone bias in various tasks, exemplified by gpt-3.5's "english-first" approach when translating irish/gaeilge @xcite and its inability to pass indonesian primary school exams @xcite while simultaneously passing u.s. college-entry exams @xcite . evidently, under-representation in training corpora can adversely affect language model performance across various tasks and contexts. in this work, we quantify the "big city bias" by evaluating salary, employer presence, and commute duration predictions in the 384 metropolitan statistical areas (msas) defined by the united states census bureau. across all benchmarks, analysis indicates a correlation between msa population and predictive accuracy, indicating superior language model efficacy in the context of larger cities. given the concentration of technical talent in the united states' 10 most populous metropolitan areas foot_0 , these findings might inspire ai practitioners to look beyond so-called "tech hubs" when applying llm-generated synthetic data to geographically diverse job matching applications. we contribute: ## results experimental results are displayed in nearly all experiments demonstrate negative and significant pearson coefficients, indicating that llms tend to achieve better performance in tasks related to larger cities. commute duration tasks generally show less stable outcomes, with lower correlations and a broader range of median errors, whereas salary prediction tasks consistently present higher correlations and smaller median errors. median errors are highest for employer presence predictions, which possibly relates to its distribution being the most skewed (figure 1 ). ## related work research using the open-source popqa dataset shows that llms exhibit worse performance at tasks associated with "popular" entities. @xcite additionally, a separate study reveals a correlation between a nation's gdp-per-capita and performance on related llm tasks. @xcite while both studies emphasize disparities in llm task performance based on popularity and/or geography, neither assesses hr-related task performance across metropolitan areas. ## conclusion large language models, while powerful, show suboptimal performance in predicting salaries, commute duration, and employer presence in specific regions, with this trend worsening in smaller areas. while llms seem unsuitable at generating such job matching data, practitioners should remain vigilant in mitigating geographic bias as these models undergo further development and improvement.