Augmenting Data Quality and Model Reliability in Large-Scale Language and Code Models: A Hybrid Framework for Evaluation, Pretraining, and Retrieval-Augmented Techniques

Abstract

Background: The rapid expansion of large language models (LLMs) and code-generative models has transformed

research and industry practices across natural language processing, software engineering, and data-driven decision-making. Yet, the increasing scale of datasets and repeat data exposure introduces complex challenges in data quality, training set augmentation, model reliability, and downstream evaluation (Ding, 2019; Hernandez et al., 2022). Prior work has examined whether large-scale datasets are necessary for self-supervised pretraining (El-Nouby et al., 2021), explored the landscape of open-source engineering efforts (Han et al., 2021), and surveyed retrieval-augmented language models (Hu & Lu, 2024). However, integrated frameworks that connect data augmentation, rigorous quality validation, and evaluation tailored to LLMs remain underdeveloped.

Objective: This article proposes and thoroughly elaborates a hybrid, academically rigorous framework that synthesizes data augmentation best practices, AI-augmented data quality validation, retrieval-augmented model design, and robust evaluation metrics for LLMs and code models. It aims to bridge theoretical foundations with practical design choices and provide an interpretive, evidence-based roadmap for researchers and practitioners.

Methods: We synthesize perspectives from empirical case studies on training-data augmentation (Ding, 2019), scaling laws and interpretability of repeated data (Hernandez et al., 2022), debates on dataset scale for self-supervision (El-Nouby et al., 2021), and contemporary LLM evaluation challenges (Gao et al., 2024). From these sources we construct a layered methodology: (1) Source-level data curation and provenance tracing informed by record linkage principles (Herzog et al., 2007); (2) augmentation strategies balancing synthetic and human-authored instances (Ding, 2019); (3) hybrid validation combining rule-based checks and LLM-assisted anomaly detection (Malviya & Parate, 2025); (4) design patterns for retrieval-augmented pipelines (Hu & Lu, 2024); and (5) a multi-faceted evaluation protocol incorporating statistical, qualitative, and LLM-based evaluators (Gao et al., 2024; Wang et al., 2023).

Results: The resulting framework identifies trade-offs between dataset scale and diversity, quantifies danger zones where repeated data leads to overfitting or miscalibration (Hernandez et al., 2022), and recommends concrete validation procedures to detect provenance drift, duplication bias, and label noise. We also specify evaluation batteries for code synthesis models and medical-diagnostic LLM comparisons using ensemble judge designs (Fried et al., 2022; Caruccio et al., 2024).

Conclusions: By integrating augmentation, validation, retrieval, and evaluation, the framework supports more reliable, auditable, and interpretable LLM deployments. Theoretical implications include revised perspectives on necessary dataset scale, formalization of hybrid validation agents, and suggested directions for future empirical work. This synthesis provides a substantive foundation for reproducible research and practical deployment strategies for LLMs and code models.