The paper introduces the Autonomous LLM-Augmented Causal Discovery Framework (ALCM), which aims to enhance the process of causal discovery by integrating conventional data-driven causal discovery algorithms and Large Language Models (LLMs).
The framework consists of three key components:
Causal Structure Learning: This component utilizes conventional causal discovery algorithms, such as the PC algorithm and a hybrid approach combining PC and LiNGAM, to generate an initial causal graph from observational data.
Causal Wrapper: This component translates the initial causal graph into a series of contextual, causal-aware prompts that are fed to the LLM-driven refiner component. The prompts incorporate instructions, causal context, metadata, and the desired output format to guide the LLM's understanding and refinement of the causal relationships.
LLM-driven Refiner: This component leverages advanced language models to assess, refine, and potentially augment the initial causal graph. It evaluates the causal edges and nodes, and where necessary, adds, removes, or modifies them to better represent the underlying causal mechanisms.
The authors evaluate the ALCM framework using seven well-known benchmark datasets and compare its performance against conventional causal discovery algorithms and LLM-based approaches. The results demonstrate that ALCM outperforms existing methods in terms of precision, recall, F1-score, accuracy, and Normalized Hamming Distance, indicating its ability to generate more accurate and reliable causal graphs.
The paper highlights the potential of leveraging the causal reasoning capabilities of LLMs in conjunction with conventional causal discovery algorithms to address the limitations of each approach and deliver a more comprehensive and robust causal discovery solution.
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by Elahe Khatib... في arxiv.org 05-06-2024
https://arxiv.org/pdf/2405.01744.pdfاستفسارات أعمق