Using Text-Based Life Trajectories from Swedish Register Data to Predict Residential Mobility with Pretrained Transformers

• URL: http://arxiv.org/abs/2512.07865v1
• Date: Wed, 26 Nov 2025 15:27:32 GMT
• Title: Using Text-Based Life Trajectories from Swedish Register Data to Predict Residential Mobility with Pretrained Transformers
• Abstract summary: We convert register data from 6.9 million individuals (2001-2013) into semantically rich texts.<n>These life trajectories combine demographic information with annual changes in residence, work, education, income, and family circumstances.
• Score: 39.146761527401424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We transform large-scale Swedish register data into textual life trajectories to address two long-standing challenges in data analysis: high cardinality of categorical variables and inconsistencies in coding schemes over time. Leveraging this uniquely comprehensive population register, we convert register data from 6.9 million individuals (2001-2013) into semantically rich texts and predict individuals' residential mobility in later years (2013-2017). These life trajectories combine demographic information with annual changes in residence, work, education, income, and family circumstances, allowing us to assess how effectively such sequences support longitudinal prediction. We compare multiple NLP architectures (including LSTM, DistilBERT, BERT, and Qwen) and find that sequential and transformer-based models capture temporal and semantic structure more effectively than baseline models. The results show that textualized register data preserves meaningful information about individual pathways and supports complex, scalable modeling. Because few countries maintain longitudinal microdata with comparable coverage and precision, this dataset enables analyses and methodological tests that would be difficult or impossible elsewhere, offering a rigorous testbed for developing and evaluating new sequence-modeling approaches. Overall, our findings demonstrate that combining semantically rich register data with modern language models can substantially advance longitudinal analysis in social sciences.

Related papers

• Estimating Time Series Foundation Model Transferability via In-Context Learning [74.65355820906355]
  Time series foundation models (TSFMs) offer strong zero-shot forecasting via large-scale pre-training.<n>Fine-tuning remains critical for boosting performance in domains with limited public data.<n>We introduce TimeTic, a transferability estimation framework that recasts model selection as an in-context-learning problem.
  arXiv  Detail & Related papers  (2025-09-28T07:07:13Z)
• Statistical Comparative Analysis of Semantic Similarities and Model Transferability Across Datasets for Short Answer Grading [3.7723788828505125]
  This study investigates the transferability of state-of-the-art (SOTA) models trained on established datasets to an unexplored text dataset.<n>The primary goal of this work is to yield comprehensive insights into the potential applicability and adaptability of SOTA models.
  arXiv  Detail & Related papers  (2025-08-19T05:45:02Z)
• Personality Prediction from Life Stories using Language Models [12.851871085845499]
  In this study, we address the challenge of modeling long narrative interview where each exceeds 2000 tokens so as to predict Five-Factor Model (FFM) personality traits.<n>We propose a two-step approach: first, we extract contextual embeddings using sliding-window fine-tuning of pretrained language models; then, we apply Recurrent Neural Networks (RNNs) with attention mechanisms to integrate long-range dependencies and enhance interpretability.
  arXiv  Detail & Related papers  (2025-06-24T02:39:06Z)
• Leveraging Multivariate Long-Term History Representation for Time Series Forecasting [6.661358934189792]
  We propose a framework called Long-term Multivariate Representation (LMHR) for MTS forecasting.<n>LMHR encodes the long-term history into segment-level contextual representations and reduces point-level noise.<n>It consistently improves prediction accuracy by 9.8% on the top 10% of rapidly changing patterns.
  arXiv  Detail & Related papers  (2025-05-20T03:46:36Z)
• Generating Realistic Tabular Data with Large Language Models [49.03536886067729]
  Large language models (LLM) have been used for diverse tasks, but do not capture the correct correlation between the features and the target variable. We propose a LLM-based method with three important improvements to correctly capture the ground-truth feature-class correlation in the real data. Our experiments show that our method significantly outperforms 10 SOTA baselines on 20 datasets in downstream tasks.
  arXiv  Detail & Related papers  (2024-10-29T04:14:32Z)
• Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolution [7.681258910515419]
  Tabular data presents unique challenges due to its heterogeneous nature and complex structural relationships. High predictive performance and robustness in tabular data analysis holds significant promise for numerous applications. The recent advent of large language models, such as GPT and LLaMA, has further revolutionized the field, facilitating more advanced and diverse applications with minimal fine-tuning.
  arXiv  Detail & Related papers  (2024-08-20T04:59:19Z)
• Evaluation of Faithfulness Using the Longest Supported Subsequence [52.27522262537075]
  We introduce a novel approach to evaluate faithfulness of machine-generated text by computing the longest noncontinuous of the claim that is supported by the context. Using a new human-annotated dataset, we finetune a model to generate Longest Supported Subsequence (LSS) Our proposed metric demonstrates an 18% enhancement over the prevailing state-of-the-art metric for faithfulness on our dataset.
  arXiv  Detail & Related papers  (2023-08-23T14:18:44Z)
• Comparing Test Sets with Item Response Theory [53.755064720563]
  We evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. We find that Quoref, HellaSwag, and MC-TACO are best suited for distinguishing among state-of-the-art models. We also observe span selection task format, which is used for QA datasets like QAMR or SQuAD2.0, is effective in differentiating between strong and weak models.
  arXiv  Detail & Related papers  (2021-06-01T22:33:53Z)
• Two-step penalised logistic regression for multi-omic data with an application to cardiometabolic syndrome [62.997667081978825]
  We implement a two-step approach to multi-omic logistic regression in which variable selection is performed on each layer separately. Our approach should be preferred if the goal is to select as many relevant predictors as possible. Our proposed approach allows us to identify features that characterise cardiometabolic syndrome at the molecular level.
  arXiv  Detail & Related papers  (2020-08-01T10:36:27Z)
• Longitudinal Variational Autoencoder [1.4680035572775534]
  A common approach to analyse high-dimensional data that contains missing values is to learn a low-dimensional representation using variational autoencoders (VAEs) Standard VAEs assume that the learnt representations are i.i.d., and fail to capture the correlations between the data samples. We propose the Longitudinal VAE (L-VAE), that uses a multi-output additive Gaussian process (GP) prior to extend the VAE's capability to learn structured low-dimensional representations. Our approach can simultaneously accommodate both time-varying shared and random effects, produce structured low-dimensional representations
  arXiv  Detail & Related papers  (2020-06-17T10:30:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.