LLM Ensemble

LLM ensembles combine multiple large language models to improve performance beyond what any single model can achieve. Current research focuses on addressing challenges like vocabulary mismatches between models, enhancing robustness against adversarial attacks, and optimizing ensemble construction for specific tasks such as medical text correction and speech recognition. This approach offers significant potential for improving accuracy, efficiency, and reliability in various applications, ranging from improving the accuracy of AI-based systems to enhancing data privacy during collaborative inference. The effectiveness of ensembling, however, is shown to depend on factors such as model type and the level of disagreement between individual models.