From Word Sequences to Behavioral Sequences: Adapting Modeling and Evaluation Paradigms for Longitudinal NLP
arXiv:2601.07988v2 Announce Type: replace-cross Abstract: While NLP typically treats documents as independent and unordered samples, in longitudinal studies, this assumption rarely holds: documents are nested within authors and ordered in time, forming person-indexed, time-ordered $\textit{behavioral sequences}$. Here, we demonstrate the need for and propose a longitudinal modeling and evaluation paradigm that consequently updates four parts of the NLP pipeline: (1) evaluation splits aligned to generalization over people ($\textit{cross-sectional}$) and/or time ($\textit{prospective}$); (2) ac
The increasing sophistication and integration of AI systems into real-world applications, particularly those involving long-term user interaction, necessitates a more robust understanding of behavioral sequences.
A refined understanding of longitudinal NLP will significantly improve the accuracy, robustness, and ethical implications of AI models dealing with human behavior over time, impacting areas from personalized medicine to social science research.
The conventional NLP paradigm, which treats documents as independent, will shift towards one that explicitly accounts for time-ordered, person-indexed behavioral sequences, leading to more contextually aware and adaptive models.
- · AI researchers and developers
- · Social scientists
- · Personalized health platforms
- · Behavioral analytics companies
- · AI models relying solely on static data
- · Traditional NLP methodologies
NLP models will become significantly better at understanding and predicting human behavior over time.
This improved understanding could lead to more effective interventions in public health, education, and personalized digital experiences.
The ethical implications of deeply understanding and potentially predicting individual behavioral trajectories will demand new regulatory and societal frameworks.
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Read at arXiv cs.LG