From Word Sequences to Behavioral Sequences: Adapting Modeling and Evaluation Paradigms for Longitudinal NLP
Adithya V Ganesan , State University of New York, Stony Brook, Vasudha Varadarajan, Oscar Kjell Vanderbilt University and Lund University, Whitney Ringwald University of Minnesota - Twin Cities, Scott M. Feltman State University of New York at Stony Brook, Benjamin J. Luft State University of New York at Stony Brook, Roman Kotov State University of New York at Stony Brook, Ryan L. Boyd University of Texas at Dallas 等 9 位作者
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) accuracy metrics separating between-person differences from within-person dynamics; (3) sequence inputs to incorporate history by default; and (4) model internals that support different \textit{coarseness} of latent state over histories (pooled summaries, explicit dynamics, or interaction-based models).We demonstrate the issues ensued by traditional pipeline and our proposed improvements on a dataset of 17k daily diary transcripts paired with PTSD symptom severity from 238 participants, finding that traditional document-level evaluation can yield substantially different and sometimes reversed conclusions compared to our ecologically valid modeling and evaluation. We tie our results to a broader discussion motivating a shift from word-sequence evaluation toward \textit{behavior-sequence} paradigms for NLP.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表