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August 17, 2025Open Access

Ensemble Large Language Models: A Survey

Key Points

Ensemble learning enhances performance in large language models, making them more robust against challenges.
Current methodologies show promise, with applications in tasks like translation, summarization, and reasoning in NLP.
This review analyzes existing challenges in large language models and explores various ensemble strategies to address them.
Insights from the survey suggest future research directions to advance the effectiveness of ensemble large language models.

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.

Ensemble Large Language Models: A Survey

Key Points

Abstract

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