发表机构
Worcester Polytechnic Institute（伍斯特理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究探讨LLM辅助正则化在低数据环境下提高移民流动预测准确性的可能性，通过从新闻中提取推拉信号并加权Lasso框架，实验显示不同走廊效果不一，最佳MAPE达17.15%。
AI 中文摘要
预测移民流动对于传统的基于重力的预测模型来说仍然是一个重大挑战，这些模型主要依赖于结构化的社会经济指标，如经济差距、政治稳定性和地理距离。本研究探讨大型语言模型（LLMs）能否通过从新闻文章中提取与移民相关的上下文信号，并将其通过特征特定的正则化惩罚纳入加权Lasso预测框架，从而提高移民预测的准确性。所提出的框架使用分层LLM推理管道来从新闻数据中分类移民相关的推拉信号，并评估了2021年11月至2022年11月期间多个移民走廊的预测性能，包括墨西哥-美国、乌克兰-波兰和叙利亚-土耳其。实验结果显示，不同移民走廊和建模策略的性能表现不一，没有一种单一的正则化方法在所有实验中始终优于其他方法。表现最佳的墨西哥配置由基于重力的模型加上所提出的推拉比率组成，实现了平均绝对百分比误差（MAPE）为17.15%，而最强的叙利亚配置使用直接LLM-Lasso，实现了MAPE为29.29%。对于乌克兰，表现最佳的配置使用了LLM辅助正则化（AR），实现了MAPE为41.05%。总体而言，结果表明，在某些条件下，上下文文章衍生的特征和LLM引导的正则化可以提高移民预测的准确性，尽管移民走廊特征、文章数量和超参数配置对性能有强烈影响。
英文摘要
Predicting migration flows remains a significant challenge for traditional gravity-based forecasting models, which primarily rely on structured socio-economic indicators such as economic disparity, political stability, and geographic distance. This work investigates whether Large Language Models (LLMs) can improve migration forecasting by extracting contextual migration-related signals from news articles and incorporating them into a weighted Lasso forecasting framework through feature-specific regularization penalties. The proposed framework uses hierarchical LLM inference pipelines to classify migration-related push--pull signals from news data and evaluates the resulting forecasting performance across multiple migration corridors between November 2021 and November 2022, including Mexico--United States, Ukraine--Poland, and Syria--Turkey. Experimental results showed mixed performance across migration corridors and modeling strategies, and no single regularization approach consistently outperformed the others across all experiments. The best-performing Mexico configuration, which consisted of a gravity-based model augmented with the proposed push--pull ratios, achieved a Mean Absolute Percentage Error (MAPE) of 17.15%, while the strongest Syria configuration achieved a MAPE of 29.29% using Direct LLM-Lasso. For Ukraine, the best-performing configuration used LLM-Assisted Regularization (AR) and achieved a MAPE of 41.05%. Overall, the results suggest that contextual article-derived features and LLM-guided regularization can improve migration forecasting under certain conditions, although migration corridor characteristics, article volume, and hyperparameter configuration strongly influenced performance.
