How Training Data Shapes the Use of Parametric and In-Context Knowledge in Language Models

ACL ARR 2026 January Submission8331 Authors

Keywords: Language Models, Knowledge Conflicts, In-context Knowledge

Abstract: Large language models leverage not only parametric knowledge acquired during training but also in-context knowledge provided at inference time, despite the absence of explicit training objectives for using both sources. Prior work has further shown that when these knowledge sources conflict, models resolve the tension based on their internal confidence, preferring parametric knowledge for high-confidence facts while deferring to contextual information for less familiar ones. However, the training conditions that give rise to such knowledge utilization behaviors remain unclear. To address this gap, we conduct controlled experiments in which we train language models while systematically manipulating key properties of the training data. Our results reveal a counterintuitive finding: three properties commonly regarded as detrimental must co-occur for robust knowledge utilization and conflict resolution to emerge—(i) intra-document repetition of information, (ii) a moderate degree of within-document inconsistency, and (iii) a skewed knowledge frequency distribution. We further validate that the same training dynamics observed in our controlled setting also arise during real-world language model pretraining, and we analyze how post-training procedures can reshape models’ knowledge preferences. Together, our findings provide concrete empirical guidance for training language models that harmoniously integrate parametric and in-context knowledge.

Paper Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: knowledge tracing, data influence

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 8331