cs.LGOct 5, 2026

Can LLM-assisted regularization increase forecast accuracy for migration flows in low data regimes?

Authors: Nathaniel T. Hindman, Fabricio Murai

Organizations: Artificial Intelligence Worcester Polytechnic Institute Worcester, Massachusetts · Data Science Worcester Polytechnic Institute Worcester, Massachusetts

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Abstract

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.

Figures & tables

CorridorMinMaxMeanStd. Dev.
Mexico–US8,718102,93946,19423,840
Ukraine–Poland998163,93718,62830,839
Syria–Turkey1,95917,3955,8213,325
TABLE I: Summary statistics for migration-flow datasets across evaluated migration corridors.
Fig. 1: SuLuSa Classification across steps
Fig. 2: SuLu Classification across steps
ModePushPullMixedNeutralUnclear
Qwen SuSa1622400195
Qwen SuLu1263510219
Qwen SuLuSa1576010163
Mistral SuSa15040150172
TABLE II: Inference Pipeline Performance at Temperature τ=0.8\tau=0.8
 GravityBestDirectLLM-AR
 Gravity-PPLLM-Lasso
Mexico17.4417.1523.4823.92
Ukraine61.6642.1043.6141.05
Syria31.4432.7429.2932.26
TABLE III: Direct LLM-Lasso performance compared to control and best gravity-based forecasting configurations (MAPE %)
Fig. 3: Box plot comparison of absolute percentage error distributions across migration forecasting models for Mexico, Syria, and Ukraine under varying article counts and regularization settings. Lower values indicate improved predictive accuracy.
Fig. 4: N-comparison across Models
Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Temp.ModePushPullMixedNeutralUnclear
0.3Qwen SuSa1522720200
Qwen SuLu1244600211
Qwen SuLuSa1645400163
Mistral SuSa1604240168
0.8Qwen SuSa1622400195
Qwen SuLu1263510219
Appendix
TABLE IV: Temperature sensitivity results.
TemperaturePushPullMixedNeutralUnclear
0.11655510160
0.31645400163
0.51734640158
0.81576010163
0.91594820170
1.51754160159
Appendix
TABLE V: Inference Performance for mode SuLuSa across temperatures
Forecasting Results
Migration CorridorBest Modelλ\boldsymbol{\lambda}NN Articles
MexicoNo wjw_{j}0.0031,245
UkraineLLM-Assisted Reg. (AR)0.003100
SyriaDirect Lasso0.005882
Appendix
TABLE VI: Best-performing forecasting configurations across migration corridors.
Hyper-ParameterEvaluated Values
Lasso Regularization Strength ( λ\lambda )7×10−47\times 10^{-4} , 9×10−49\times 10^{-4} , 1×10−31\times 10^{-3} ,
 3×10−33\times 10^{-3} , 5×10−35\times 10^{-3} , 6×10−36\times 10^{-3} ,
 7×10−37\times 10^{-3} , 8×10−38\times 10^{-3}
Primary Experimental Values{1,3,5,8}×10−3\{1,3,5,8\}\times 10^{-3}
Appendix
TABLE VII: Evaluated Lasso regularization strengths used throughout forecasting experiments.

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