用多模态数据和工具增强大模型，精准发现因果关系
Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery
- 设计双智能体系统，分别处理数据增强与因果推理
- 在7个数据集上验证，多模态信息显著提升因果发现效果
- 适合需要高可信因果分析的医疗、药物研发场景
因果发现是智能健康、药物发现和AIOps等领域决策的基础。传统统计方法依赖观测数据，常忽略因果关系中的语义线索。大语言模型（LLMs）提供了低成本利用语义信息的新路径，但其在因果发现领域的发展滞后，尤其缺乏对多模态数据的探索。为此，我们提出MATMCD，一个由工具增强的多智能体系统。该系统包含两个核心智能体：数据增强代理负责检索并处理多模态数据，因果约束代理则整合多模态信息进行知识驱动推理。内部协作机制确保了智能体间高效协同。在七个数据集上的实证研究表明，多模态增强显著提升了因果发现能力。
原文摘要 · Abstract (English)
Causal discovery is an imperative foundation for decision-making across domains, such as smart health, AI for drug discovery and AIOps. Traditional statistical causal discovery methods, while well-established, predominantly rely on observational data and often overlook the semantic cues inherent in cause-and-effect relationships. The advent of Large Language Models (LLMs) has ushered in an affordable way of leveraging the semantic cues for knowledge-driven causal discovery, but the development of LLMs for causal discovery lags behind other areas, particularly in the exploration of multi-modal data. To bridge the gap, we introduce MATMCD, a multi-agent system powered by tool-augmented LLMs. MATMCD has two key agents: a Data Augmentation agent that retrieves and processes modality-augmented data, and a Causal Constraint agent that integrates multi-modal data for knowledge-driven reasoning. The proposed design of the inner-workings ensures successful cooperation of the agents. Our empirical study across seven datasets suggests the significant potential of multi-modality enhanced causal discovery.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。