Background: The rapid evolution of large language models (LLMs) has outpaced standardized frameworks for their evaluation, deployment, and governance. Diverse evaluation protocols, emergent alignment techniques, and domain adaptation strategies have been proposed, yet a unified, theoretically grounded, and practically applicable framework that connects evaluation, fine-tuning, bias assessment, and end-to-end testing remains underdeveloped.

Objective: This article proposes and elaborates an integrated framework that synthesizes state-of-the-art evaluation methodologies, bias and truthfulness assessments, domain-specific fine-tuning practices, and automation frameworks for end-to-end testing, grounded in existing literature and empirical benchmarks. Methods: Drawing strictly on the provided literature, we construct a conceptual pipeline where standardized evaluation metrics (including human-aligned LLM-based evaluators), bias and safety assays, and domain adaptation workflows interlock to produce responsible deployment. We analytically extend evaluation taxonomies, compare model families (closed versus open foundation models), and propose best-practice procedural guidelines for automated testing.

Results: The framework clarifies relationships among intrinsic metrics (e.g., perplexity and proxy linguistic measures), extrinsic metrics (task performance), human-aligned automated evaluation (G-Eval and OmniEvalKit principles), and qualitative safety/bias tests (StereoSet, CrowS-Pairs, TruthfulQA). We articulate methodological choices for domain fine-tuning and provide an automation blueprint for continuous validation and regression testing in production settings.

Conclusions: The proposed integrated framework operationalizes evaluation and governance for LLMs, balancing performance optimization with societal risk mitigation. Adoption of this framework can reduce deployment failures, improve alignment with human judgments, and create a replicable pipeline for domain-specialized LLMs. Limitations include dependence on evolving evaluation tools and the need for empirical calibration in diverse application domains. The article closes with prioritized avenues for future research, including benchmark harmonization and adaptive testing regimes.

Large language models, evaluation framework, bias assessment

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