Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery
利用工具增强的大语言模型智能体开展多模态因果发现
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,…