Bridging Causal Discovery and Large Language Models: A Comprehensive Survey of Integrative Approaches and Future Directions

arXiv:2402.11068v1 Announce Type: new
Abstract: Causal discovery (CD) and Large Language Models (LLMs) represent two emerging fields of study with significant implications for artificial intelligence. Despite their distinct origins, CD focuses on uncovering cause-effect relationships from data, and LLMs on processing and generating humanlike text, the convergence of these domains offers novel insights and methodologies for understanding complex systems. This paper presents a comprehensive survey of the integration of LLMs, such as GPT4, into CD tasks. We systematically review and …

abstract artificial artificial intelligence arxiv cs.ai cs.cl data discovery fields future humanlike intelligence language language models large language large language models llms processing relationships study survey type