A Conceptual Framework for Context-Aware Data Curation in Fine-Tuning Pipelines
Abstract
The performance, safety, and reliability of fine-tuned artificial intelligence models depend not only on model architecture and optimization strategy but also on whether training data accurately represent the contexts in which models will operate. Conventional data-curation pipelines frequently prioritize volume, generic quality indicators, and static filtering while overlooking task intent, domain conditions, user populations, temporal change, deployment constraints, and risk. This review develops a conceptual framework for context-aware data curation in fine-tuning pipelines by integrating contextual specification, data discovery, provenance analysis, quality assessment, representation auditing, adaptive selection, and post-deployment feedback. The framework defines context through task objectives, domain semantics, stakeholder requirements, language and cultural variation, regulatory obligations, operating environments, and anticipated failure consequences. It examines how metadata, embeddings, knowledge graphs, uncertainty measures, influence functions, and data-valuation methods can identify examples that are relevant, diverse, informative, and safe. Particular attention is given to duplicate detection, label reliability, distribution imbalance, contamination, privacy leakage, harmful content, temporal obsolescence, and conflicts between data sources. The paper compares rule-based filtering, human review, active learning, curriculum design, retrieval-assisted selection, and model-guided curation, emphasizing their trade-offs in scalability, explainability, cost, and bias. It further proposes feedback mechanisms that connect evaluation failures, user interactions, drift signals, and safety incidents to targeted dataset revision rather than indiscriminate data expansion. Evaluation criteria include downstream utility, calibration, robustness, fairness, memorization risk, provenance completeness, curation efficiency, and reproducibility. Key challenges involve incomplete metadata, circular model-based judgments, changing contexts, subjective quality standards, and governance across distributed data supply chains. The resulting framework positions data curation as a continuous, auditable, and risk-sensitive control process linking intended use to model behavior. It provides researchers and practitioners with a structured basis for designing fine-tuning datasets that improve contextual alignment while reducing avoidable bias, fragility, privacy exposure, and maintenance cost across evolving artificial intelligence applications in practice.
Keywords
Context-aware curation; data governance; data provenance; dataset quality; fine- tuning pipelines; responsible artificial intelligence
References
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