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2024

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

Ashwani, Swagata, Hegde, Kshiteesh, Mannuru, Nishith Reddy et al.

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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 CA) framework to enhance causal reasoning and explainability.
The proposed framework incorporates an explicit causal detection module with ConceptNet and counterfactual statements, as well as implicit causal detection through LLMs.

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https://platform.openai.com/docs

OpenAI. 2024 · 2024

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