This research explores how ensemble learning can address inherent limitations of individual large language models by combining their strengths to produce more reliable and diverse outputs.

• Combines multiple LLM outputs to mitigate inconsistencies and reduce biases
• Enables organizations to leverage closed-source models while maintaining data integration capabilities
• Improves both text and code generation quality through diverse model combinations
• Particularly valuable for creative applications requiring consistent, high-quality content generation

For creative professionals, this approach offers more reliable content generation tools that can produce diverse outputs while reducing the limitations of any single model's inherent biases.

Ensemble Learning for Large Language Models in Text and Code Generation: A Survey