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Can large language models build causal graphs?
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Building causal graphs can be a laborious process. To ensure all relevant causal pathways have been captured, researchers often have to discuss with clinicians and experts while also reviewing extensive relevant medical literature. By encoding common and medical knowledge, large language models (LLMs) represent an opportunity to ease this process by automatically scoring edges (i.e., connections between two variables) in potential graphs. LLMs however have been shown to be brittle to the choice of probing words, context, and prompts that the user employs. In this work, we evaluate if LLMs can be a useful tool in complementing causal graph development.
Forward citations
Cited by 3 Pith papers
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Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
LLMs perform much worse on causal questions built from post-cutoff news articles, suggesting their apparent causal skill is mostly memorization, and a general-knowledge prompt method only partly closes the gap.
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Causal Graph based Event Reasoning using Semantic Relation Experts
A multi-agent LLM debate with four semantic-relation experts builds causal event graphs that improve explainable event likelihood prediction and match fine-tuned models on forecasting and next-event prediction.
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Causal MAS: A Survey of Large Language Model Architectures for Discovery and Effect Estimation
A structured survey that defines and catalogs multi-agent LLM systems for causal reasoning, discovery, and effect estimation, including their architectures, benchmarks, and applications.
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