Bridging Causal Discovery and Large Language Models: A Comprehensive Survey of Integrative Approaches and Future Directions
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 compare existing approaches that leverage LLMs for various CD tasks and highlight their innovative use of metadata and natural language to infer causal structures. Our analysis reveals the strengths and potential of LLMs in both enhancing traditional CD methods and as an imperfect expert, alongside the challenges and limitations inherent in current practices. Furthermore, we identify gaps in the literature and propose future research directions aimed at harnessing the full potential of LLMs in causality research. To our knowledge, this is the first survey to offer a unified and detailed examination of the synergy between LLMs and CD, setting the stage for future advancements in the field.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal DiscoverySurveySimilar Papers 제목 키워드 기반
From Pre-training Corpora to Large Language Models: What Factors Influence LLM Performance in Causal Discovery Tasks?
Recent advances in artificial intelligence have seen Large Language Models (LLMs) demonstrate notable proficiency in causal discovery tasks. This study explores the factors influencing the performance of LLMs in causal d…
Causal DiscoveryCausal-learn: Causal Discovery in Python
Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe $\textit{causal-learn}$, an open-source Python library for causal discovery…
Causal DiscoveryBeyond Correlation: Towards Causal Large Language Model Agents in Biomedicine
Large Language Models (LLMs) show promise in biomedicine but lack true causal understanding, relying instead on correlations. This paper envisions causal LLM agents that integrate multimodal data (text, images, genomics,…
Causal InferenceDrug DiscoveryLanguage ModelingLanguage Modelling+1OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Framework
Large language models (LLMs) have excelled in various natural language processing tasks, but challenges in interpretability and trustworthiness persist, limiting their use in high-stakes fields. Causal discovery offers a…
Causal DiscoveryRevealing Multimodal Causality with Large Language Models
Uncovering cause-and-effect mechanisms from data is fundamental to scientific progress. While large language models (LLMs) show promise for enhancing causal discovery (CD) from unstructured data, their application to the…