A Tutorial on Multi-Armed Bandit Applications for Large Language Models

Abstract

This tutorial offers a comprehensive guide on using multi-armed bandit (MAB) algorithms to improve Large Language Models (LLMs). As Natural Language Processing (NLP) tasks grow, efficient and adaptive language generation systems are increasingly needed. MAB algorithms, which balance exploration and exploitation under uncertainty, are promising for enhancing LLMs. The tutorial covers foundational MAB concepts, including the exploration-exploitation trade-off and strategies like epsilon-greedy, UCB (Upper Confidence Bound), and Thompson Sampling. It then explores integrating MAB with LLMs, focusing on designing architectures that treat text generation options as arms in a bandit problem. Practical aspects like reward design, exploration policies, and scalability are discussed. Real-world case studies demonstrate the benefits of MAB-augmented LLMs in content recommendation, dialogue generation, and personalized content creation, showing how these techniques improve relevance, diversity, and user engagement.