Large Language Models (LLMs) have demonstrated impressive performance in executing complex reasoning tasks. Chain-of-thought effectively enhances reasoning capabilities by unlocking the potential of large models, while multi-agent systems provide more comprehensive solutions by integrating the collective intelligence of multiple agents. However, both approaches face significant limitations. Single-agent with chain-of-thought, due to the inherent complexity of designing cross-domain prompts, faces collaboration challenges. Meanwhile, multi-agent systems consume substantial tokens and inevitably dilute the primary problem, which is particularly problematic in business workflow tasks. To address these challenges, we propose Cochain, a collaboration prompting framework that effectively solves the business workflow collaboration problem by combining knowledge and prompts at a reduced cost. Specifically, we construct an integrated knowledge graph that incorporates knowledge from multiple stages. Furthermore, by maintaining and retrieving a prompts tree, we can obtain prompt information relevant to other stages of the business workflow. We perform extensive evaluations of Cochain across multiple datasets, demonstrating that Cochain outperforms all baselines in both prompt engineering and multi-agent LLMs. Additionally, expert evaluation results indicate that the use of a small model in combination with Cochain outperforms GPT-4.

翻译：大型语言模型（LLM）在执行复杂推理任务方面已展现出卓越性能。思维链通过释放大模型潜力有效增强了推理能力，而多智能体系统则通过整合多个智能体的集体智慧提供了更全面的解决方案。然而，这两种方法均存在显著局限性。采用思维链的单智能体由于设计跨领域提示词存在固有复杂性，面临协作挑战；而多智能体系统则需消耗大量令牌资源，且不可避免地稀释核心问题，这在业务工作流任务中尤为突出。为应对这些挑战，我们提出Cochain——一种协作提示框架，该框架通过以较低成本整合知识与提示词，有效解决了业务工作流协作问题。具体而言，我们构建了融合多阶段知识的集成知识图谱，并通过维护与检索提示树，可获取与业务工作流其他阶段相关的提示信息。我们在多个数据集上对Cochain进行了广泛评估，结果表明Cochain在提示工程与多智能体LLM方面均优于所有基线方法。此外，专家评估结果显示：小型模型结合Cochain框架的表现优于GPT-4。