  We present substantial evidence demonstrating the benefits of integrating
Large Language Models (LLMs) with a Contextual Multi-Armed Bandit framework.
Contextual bandits have been widely used in recommendation systems to generate
personalized suggestions based on user-specific contexts. We show that LLMs,
pre-trained on extensive corpora rich in human knowledge and preferences, can
simulate human behaviours well enough to jump-start contextual multi-armed
bandits to reduce online learning regret. We propose an initialization
algorithm for contextual bandits by prompting LLMs to produce a pre-training
dataset of approximate human preferences for the bandit. This significantly
reduces online learning regret and data-gathering costs for training such
models. Our approach is validated empirically through two sets of experiments
with different bandit setups: one which utilizes LLMs to serve as an oracle and
a real-world experiment utilizing data from a conjoint survey experiment.
