Exploring Multi-Modal Integration with Tool-Augmented LLM Agents for Precise Causal Discovery

• URL: http://arxiv.org/abs/2412.13667v1
• Date: Wed, 18 Dec 2024 09:50:00 GMT
• Title: Exploring Multi-Modal Integration with Tool-Augmented LLM Agents for Precise Causal Discovery
• Authors: ChengAo Shen, Zhengzhang Chen, Dongsheng Luo, Dongkuan Xu, Haifeng Chen, Jingchao Ni,
• Abstract summary: Causal inference is an imperative foundation for decision-making across domains, such as smart health, AI for drug discovery and AIOps. We introduce MATMCD, a multi-agent system powered by tool-augmented LLMs. Our empirical study suggests the significant potential of multi-modality enhanced causal discovery.
• Score: 45.777770849667775
• License:
• Abstract: Causal inference 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-modality 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 inference. Delicate 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.

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