Enhancing Fine-Tuning LLM Evaluation: A Study on Calibration and Metrics for Industry-Specific AI Alignment

Evaluating Large Language Models (LLMs) for AI alignment necessitates methodologies that go beyond general-purpose benchmarks to address domain-specific challenges and ethical complexities. This study investigates alignment metrics tailored to industry-specific contexts, utilizing large datasets from subject matter experts and synthetic data to fine-tune LLMs. Existing metrics, when used out-of-the-box, often fail to offer actionable insights or maintain relevance in real-world applications. To mitigate this, we evaluate a range of AI alignment metrics, including Correctness, Faithfulness, Completeness, Conciseness, Harmfulness, and Maliciousness for fine-tuning QnA data tuples and fine-tuned LLM's responses after training. Furthermore, we address issues of disparate impacts and historical biases to improve the quality and fairness of QA pair generation. Grounded in a theoretical framework, we propose that AI alignment requires a dual approach, integrating general and domain-specific evaluation methodologies. Experimental results from fine-tuning LLMs in the Banking BIAN domain reveal significant shortcomings in existing frameworks. In this paper we propose an approach to emphasize and mitigate these gaps, ensuring nuanced and ethically grounded evaluation practices. This work advances alignment methodologies aimed at fostering transparency, trustworthiness, and responsible LLM deployment in high-stakes, regulated domains.