Title: Connecting the Dots: A Chain-of-Collaboration Prompting Framework for LLM Agents
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Multi-Agent Collaboration, LLM Agents, Business Workflow Decision Making

Abstract: 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. The codes are available at https://anonymous.4open.science/r/Cochain-6866.

TL;DR: We propose Cochain, a collaboration prompting framework that combines chain-of-thought reasoning depth with multi-agent collaboration breadth to solve business workflow challenges.