Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation

arXiv cs.CL / 4/27/2026

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Key Points

The paper addresses a core temporal-mismatch problem where models trained on past data are deployed on future data with changing semantic distributions and evolving domain knowledge.
It proposes KARITA (Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation), which combines knowledge-driven augmentation and retrieval to model multiple kinds of temporal shifts (such as uncertainty and feature shifts).
KARITA builds rich knowledge sources—including medical ontology like MeSH—and integrates them to improve temporal adaptation during select-and-retrieve augmented learning.
Experiments on classification tasks across clinical, legal, and scientific datasets show consistent improvements, suggesting knowledge integration is especially important for temporal augmentation and learning.

Abstract

Time introduces fundamental challenges in model development and deployment: models are usually trained on historical data while deployed on future data where semantic distributions and domain knowledge may evolve. Unfortunately, existing studies either overlook temporal shifts or hardly capture rich shifting patterns of both semantic and knowledge. We develop Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation (KARITA) to capture diverse temporal shifts (e.g., uncertainty and feature shift), construct and integrate rich knowledge sources (e.g., medical ontology like MeSH), and leverage shifting insights for selecting-retrieval augmented learning. We evaluate KARITA on classification tasks across multiple domains, clinical, legal, and scientific corpora, demonstrating consistent improvements across multiple domains with temporal adaptation. Our results show that knowledge integration can be more critical and effective in temporal augmentation and learning.