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Causal Reasoning in Large Language Models: A Knowledge Graph Approach
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This method leverages a knowledge graph's structured causal relationships by performing a multi-step random walk to extract contextually relevant information. This extracted information, including verbalized triples and randomly selected subsequent nodes, is integrated into LLM prompts during the inference stage to enhance the LLM's reasoning abilities and performance in commonsense question answering tasks without requiring retraining.
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