OPTIMIZING AI ALGORITHMS: AN EMPIRICAL STUDY OF FEATURE ENGINEERING, FINE-TUNING, AND EVALUATION STRATEGIES

Abstract

This comprehensive article explores cutting-edge techniques for optimizing machine learning models, with a particular focus on advanced strategies for enhancing AI algorithms and large language models (LLMs). We begin by examining the critical role of feature engineering and selection in model performance, emphasizing the importance of word embeddings in natural language processing tasks. The article then delves into hyperparameter optimization methods, including grid search, random search, and Bayesian optimization, alongside tools that automate these processes. We introduce Spectrum, a novel framework for LLM training optimization that addresses key challenges in learning rate selection, batch size scaling, and distributed training. A significant portion of the article is dedicated to advanced techniques for LLMs, including fine-tuning strategies, the implementation of Retrieval-Augmented Generation (RAG), and the application of Direct Preference Optimization (DPO) for aligning model outputs with human preferences. We also discuss the Quantized Low-Rank Adaptation (QLoRA) method for efficient fine-tuning of LLMs and introduce LASER, a learning-accelerated spectrum-based data sampling approach for efficient pre-training. The article explores emerging trends such as focused vocabulary expansion for improving model efficiency on specialized tasks. We conclude with an analysis of model evaluation strategies, highlighting both general and NLP-specific metrics. By synthesizing these diverse optimization approaches, this article provides data scientists and AI researchers with a comprehensive toolkit for enhancing the performance, efficiency, and applicability of their machine learning models across various domains.