Multi-Armed Bandits Meet Large Language Models

Bandit algorithms and Large Language Models (LLMs) have traditionally been studied in separate domains---decision-making under uncertainty and natural language processing, respectively. This talk explores their emerging synergy and the transformative potential that arises when these paradigms intersect. On one hand, bandit algorithms can enhance LLM efficiency through fine-tuning, prompt optimization, adaptive response generation, and evaluation strategies. On the other, LLMs can enrich bandit methods by providing contextual understanding, adaptive policies, predictive insights, and natural language--driven feedback. I will present a survey of state-of-the-art research, highlight promising applications in personalization, dialogue systems, autonomous agents, and healthcare, and discuss open challenges around scalability, interpretability, and multi-agent coordination. The goal is to provide a roadmap for interdisciplinary research at the intersection of bandits and LLMs, pointing toward more adaptive, human-centered, and trustworthy AI systems.