Large language models (LLMs) have exhibited remarkable capabilities and achieved significant breakthroughs across various domains, leading to their widespread adoption in recent years. Building on this progress, we investigate their potential in the realm of local life services. In this study, we establish a comprehensive benchmark and systematically evaluate the performance of diverse LLMs across a wide range of tasks relevant to local life services. To further enhance their effectiveness, we explore two key approaches: model fine-tuning and agent-based workflows. Our findings reveal that even a relatively compact 7B model can attain performance levels comparable to a much larger 72B model, effectively balancing inference cost and model capability. This optimization greatly enhances the feasibility and efficiency of deploying LLMs in real-world online services, making them more practical and accessible for local life applications.

翻译：大语言模型（LLMs）已展现出卓越的能力，并在多个领域取得重大突破，近年来得到广泛应用。基于此进展，我们探究其在本地生活服务领域的潜力。本研究构建了一个综合性评测基准，系统评估了多种大语言模型在本地生活服务相关广泛任务上的性能。为进一步提升其效能，我们探索了两种关键方法：模型微调与基于智能体的工作流。研究结果表明，即使是一个相对紧凑的7B参数模型，也能达到与庞大72B参数模型相当的性能水平，有效平衡了推理成本与模型能力。这一优化显著提升了在实际在线服务中部署大语言模型的可行性与效率，使其在本地生活应用中更具实用性和可及性。