Fine-Tuning and Evaluation of LLMs

Abstract

This chapter explains why fine-tuning is essential for adapting large language models (LLMs) to specialized and high-stakes domains, where general-purpose prompting may be unreliable. This chapter introduces fine-tuning as the process of updating a pretrained model with task-specific data and reviews evidence from healthcare, cybersecurity, ontology engineering, and transportation showing that fine-tuned models achieve higher accuracy, domain compliance, and practical usefulness. This chapter emphasizes that specialization must be paired with rigorous evaluation, highlighting current gaps in reporting on safety, fairness, and bias, and motivating the development of multidimensional assessment frameworks. It then presents a step-by-step data preparation pipeline for collection, cleaning, formatting, splitting, and privacy/bias checks, illustrated using a small synthetic university frequently asked question (FAQ) dataset. Next, it compares major fine-tuning strategies, including full-model fine-tuning, parameter-efficient methods such as low-rank adaptation (LoRA), and instruction tuning. A hands-on walkthrough demonstrates fine-tuning DistilGPT2 with LoRA and interpreting trainable-parameter efficiency. Finally, this chapter introduces intrinsic metrics (BLEU, ROUGE, METEOR, perplexity) and extrinsic metrics (accuracy, precision, recall, F 1) to assess robustness and real-world readiness.