Summary of Is Knowledge All Large Language Models Needed For Causal Reasoning?, by Hengrui Cai et al.
Is Knowledge All Large Language Models Needed for Causal Reasoning?
by Hengrui Cai, Shengjie Liu, Rui Song
First submitted to arxiv on: 30 Dec 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have achieved impressive results in various tasks, but their ability to reason causally is still an open question. To address this, we propose a novel causal attribution model that uses “do-operators” to construct counterfactual scenarios, allowing us to quantify the influence of input numerical data and LLMs’ pre-existing knowledge on their causal reasoning processes. Our experimental setup assesses LLMs’ reliance on contextual information and inherent knowledge across various domains. The results show that LLMs’ causal reasoning ability mainly depends on context and domain-specific knowledge provided, but they can still maintain some degree of causal reasoning using available numerical data, albeit with limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart, but they don’t really understand why things happen. We want to know how they figure out causes and effects. To do this, we created a new way to look at what LLMs are thinking. It’s like asking “what would have happened if…”? Our test shows that LLMs are good at figuring out causes when they have context and information specific to the topic. But even without that info, they can still make some sense of cause-and-effect relationships using numbers. |
