Model Collaboration

Model collaboration explores methods for combining the strengths of multiple machine learning models, aiming to improve performance, efficiency, and robustness beyond what single models can achieve. Current research focuses on integrating large language models (LLMs) with smaller, more efficient models for tasks like question answering, text generation, and object recognition, often employing techniques like iterative refinement, knowledge boosting, and brainstorming to achieve consensus or enhanced accuracy. This approach holds significant promise for advancing various fields, from improving the efficiency of complex reasoning tasks to enhancing the reliability and trustworthiness of AI systems in critical applications.