Cause and Effect: Can Large Language Models Truly Understand Causality?

arXiv:2402.18139v1 Announce Type: new
Abstract: With the rise of Large Language Models(LLMs), it has become crucial to understand their capabilities and limitations in deciphering and explaining the complex web of causal relationships that language entails. Current methods use either explicit or implicit causal reasoning, yet there is a strong need for a unified approach combining both to tackle a wide array of causal relationships more effectively. This research proposes a novel architecture called Context Aware Reasoning Enhancement with Counterfactual Analysis(CARE …

abstract arxiv become capabilities causality cause and effect cs.ai cs.cl current language language models large language large language models limitations llms reasoning relationships type web