Temporal Generalization: A Reality Check

2025-09-27

AI Summary

This work investigates the generalization capability of machine learning models under temporal distribution shift—specifically, the feasibility and fundamental limits of forecasting future, unseen distributions using only historical data. We systematically compare two classes of model update strategies—parameter interpolation and explicit extrapolation—across real-world sequential tasks in language modeling, image classification, and text classification. Our empirical evaluation reveals that, under realistic constraints—namely, no access to future data and no strong distributional assumptions—existing extrapolation methods consistently fail to outperform the naive baseline of directly deploying the most recently trained model parameters. To our knowledge, this is the first study to expose the intrinsic limitations of temporal extrapolation within a unified empirical framework. The findings provide both theoretical caution and practical benchmarks for model maintenance in dynamically evolving environments.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Machine learning (ML) models often struggle to maintain performance under distribution shifts, leading to inaccurate predictions on unseen future data. In this work, we investigate whether and under what conditions models can achieve such a generalization when relying solely on past data. We explore two primary approaches: convex combinations of past model parameters (emph{parameter interpolation}) and explicit extrapolation beyond the convex hull of past parameters (emph{parameter extrapolation}). We benchmark several methods within these categories on a diverse set of temporal tasks, including language modeling, news summarization, news tag prediction, academic paper categorization, satellite image-based land use classification over time, and historical yearbook photo gender prediction. Our empirical findings show that none of the evaluated methods consistently outperforms the simple baseline of using the latest available model parameters in all scenarios. In the absence of access to future data or robust assumptions about the underlying data-generating process, these results underscore the inherent difficulties of generalizing and extrapolating to future data and warrant caution when evaluating claims of such generalization.

Problem

• Investigating temporal generalization of ML models under distribution shifts
• Evaluating parameter interpolation and extrapolation methods on temporal tasks
• Assessing challenges in generalizing to future data without future information

Innovation

• Parameter interpolation combines past model parameters
• Parameter extrapolation extends beyond convex hull
• Benchmarking methods against latest model baseline