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

Individual large language models (LLMs, AI systems trained to generate text or code) often produce inconsistent outputs and show biases, which limits their usefulness. This survey examines ensemble learning techniques (methods that combine multiple models to improve results), categorizing seven approaches like weight merging (combining model parameters), mixture-of-experts (routing inputs to specialized models), and output ensemble (combining multiple model outputs), to show how combining LLMs can improve output quality and diversity in both text and code generation.