Researchers have developed collaborative strategies to address the diversity of large language models (LLMs), which often exhibit distinct strengths and weaknesses due to differences in their training corpora. The paper "Merge, Ensemble, and Cooperate" highlights three primary approaches: merging, ensemble, and cooperation. Merging involves integrating multiple LLMs into a single model, while ensemble strategies focus on combining their outputs to generate a high-quality result. Cooperation encompasses various techniques where LLMs collaborate to achieve specific objectives, leveraging their unique strengths. These collaborative strategies offer innovative ways to maximize the capabilities of LLMs, but real-world applications require balancing performance, cost, and latency.