COAT: Using Large Language Models to Improve Causal Discovery with Unstructured Data

Kernekoncepter

Large language models (LLMs) can be effectively integrated into causal discovery workflows to extract meaningful insights from unstructured data by proposing relevant high-level factors and iteratively refining them through feedback from the causal discovery algorithm.

Resumé

• Bibliographic Information: Liu, C., Chen, Y., Liu, T., Gong, M., Cheng, J., Han, B., & Zhang, K. (2024). Discovery of the Hidden World with Large Language Models. Advances in Neural Information Processing Systems, 38.
• Research Objective: This paper introduces COAT (Causal representatiOn AssistanT), a novel framework that leverages LLMs to enhance causal discovery from unstructured data by proposing and refining high-level factors.
• Methodology: COAT employs an iterative approach where LLMs propose potential factors from unstructured data. These factors are then parsed and used by a causal discovery algorithm (e.g., FCI) to infer causal relationships. The results are then fed back to the LLM to refine the proposed factors in subsequent iterations.
• Key Findings: COAT demonstrates superior performance in identifying relevant factors and uncovering causal structures compared to traditional causal discovery methods and direct LLM-based reasoning. The authors introduce two novel metrics, "perception" and "capacity," to quantify the causal reasoning abilities of LLMs.
• Main Conclusions: Integrating LLMs into causal discovery pipelines like COAT significantly improves the ability to extract meaningful causal insights from unstructured data, opening new avenues for research and applications in various domains.
• Significance: This research bridges the gap between unstructured data and causal discovery by utilizing the power of LLMs, potentially revolutionizing fields reliant on understanding causal relationships from complex data sources.
• Limitations and Future Research: The performance of COAT is influenced by the capabilities of the chosen LLM and the quality of the prompts. Future research could explore more sophisticated prompt engineering techniques and investigate the impact of different causal discovery algorithms within the COAT framework.

Statistik

GPT-4 achieved 80% recall, 93% precision, and 85% F1 score in identifying factors forming the Markov Blanket of the target variable in the AppleGastronome benchmark.
GPT-3.5 achieved 73% recall, 100% precision, and 84% F1 score in identifying factors forming the Markov Blanket of the target variable in the AppleGastronome benchmark.
LLaMA2-70B achieved 60% recall, 83% precision, and 69% F1 score in identifying factors forming the Markov Blanket of the target variable in the AppleGastronome benchmark.
Mistral-Medium achieved 93% recall, 100% precision, and 96% F1 score in identifying factors forming the Markov Blanket of the target variable in the AppleGastronome benchmark.

Citater

"The lack of high-quality high-level variables has been a longstanding impediment to broader real-world applications of CDs or causality-inspired methods."
"Trained from massive observations of the world, LLMs demonstrate impressive capabilities in comprehending unstructured inputs, and leveraging the learned rich knowledge to resolve a variety of general tasks."
"To the best of our knowledge, we are the first to leverage LLMs to propose high-level variables, thereby extending the scope of CDs to unstructured data."

Vigtigste indsigter udtrukket fra

Discovery of the Hidden World with Large Language Models

by Chenxi Liu, ... kl. arxiv.org 11-01-2024

https://arxiv.org/pdf/2402.03941.pdf