Ensemble Large Language Models: A Survey

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…