Abstract
Large language models (LLMs) have transformed the field of natural language processing
(NLP), achieving state-of-the-art performance in tasks such as translation, summarization,
and reasoning. Despite their impressive capabilities, challenges persist, including biases,
limited interpretability, and resource-intensive training. Ensemble learning, a technique
that combines multiple models to improve performance, presents a promising avenue for
addressing these limitations in LLMs. This review explores the emerging field of ensemble
LLMs, providing a comprehensive analysis of current methodologies, applications across
diverse domains, and existing challenges. By reviewing ensemble strategies and evaluating
their effectiveness, this paper highlights the potential of ensemble LLMs to enhance
robustness and generalizability while proposing future research directions to advance
the field.