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Life Trajectories as Symbolic Language ; Exploring Human Behaviour with Language Models

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Title: Life Trajectories as Symbolic Language ; Exploring Human Behaviour with Language Models
Authors: Savcisens, Germans
Contributors: Savcisens, Germans
Publisher Information: Techical University of Denmark
Publication Year: 2024
Collection: Zenodo
Subject Terms: Natural Language Processing, Neural Networks, Computer, Deep Learning/ethics, Deep learning, Deep Learning/statistics & numerical data, Machine Learning, Mortality, Demography/statistics & numerical data, Demography, Data science, Computer and information sciences, Mathematics, Mathematics/methods, Artificial intelligence, Artificial Intelligence/statistics & numerical data, Data analysis
Description: Deep learning has significantly advanced research within natural language processing in recent years. Beyond language, novel transformer-based architectures have shown promise as tools for modeling various multivariate sequences. These include weather patterns, musical compositions, and protein structures. Similarly, human lives represent another form of multivariate sequences comprising various events: People are born, attend kindergarten, visit doctors, relocate to new cities, and more.By drawing parallels between human lives and written language, we propose a novel methodology to study individual life trajectories. We use the Danish National Registry to create an artificial symbolic language. It transforms socioeconomic and health events into a structured, sentence-like format, akin to the words and sentences in a language.This representation approach lays the groundwork for our primary contribution: developing the life2vec model, a transformer-based model designed for analyzing life trajectories. In the thesis, we demonstrate that the life2vec model captures complex relationships between various life events and uses this knowledge to provide insights into early mortality and personality. A key strength of life2vec lies in interpretability, as we can use it to explore the influence of socioeconomic and health factors on individual life paths.The findings from this thesis underscore the potential of transformer models in understanding and predicting human behavior and experiences.
Document Type: text
Language: English
Relation: arXiv:2306.03009; https://zenodo.org/records/14706679; oai:zenodo.org:14706679; https://doi.org/10.5281/zenodo.14706679
DOI: 10.5281/zenodo.14706679
Availability: https://doi.org/10.5281/zenodo.14706679
https://zenodo.org/records/14706679
https://orbit.dtu.dk/en/publications/life-trajectories-as-symbolic-language
Rights: Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode ; General Rights
Accession Number: edsbas.ABED96F2
Database: BASE
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  Data: Deep learning has significantly advanced research within natural language processing in recent years. Beyond language, novel transformer-based architectures have shown promise as tools for modeling various multivariate sequences. These include weather patterns, musical compositions, and protein structures. Similarly, human lives represent another form of multivariate sequences comprising various events: People are born, attend kindergarten, visit doctors, relocate to new cities, and more.By drawing parallels between human lives and written language, we propose a novel methodology to study individual life trajectories. We use the Danish National Registry to create an artificial symbolic language. It transforms socioeconomic and health events into a structured, sentence-like format, akin to the words and sentences in a language.This representation approach lays the groundwork for our primary contribution: developing the life2vec model, a transformer-based model designed for analyzing life trajectories. In the thesis, we demonstrate that the life2vec model captures complex relationships between various life events and uses this knowledge to provide insights into early mortality and personality. A key strength of life2vec lies in interpretability, as we can use it to explore the influence of socioeconomic and health factors on individual life paths.The findings from this thesis underscore the potential of transformer models in understanding and predicting human behavior and experiences.
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