Summary of Causal Inference with Large Language Model: a Survey, by Jing Ma
Causal Inference with Large Language Model: A Survey
by Jing Ma
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper reviews recent advancements in applying large language models (LLMs) to traditional causal inference tasks across various domains. It summarizes main causal problems and approaches, presenting a comparison of evaluation results in different scenarios. The authors discuss key findings and outline directions for future research, highlighting the potential implications of integrating LLMs in advancing causal inference methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can help us understand cause-and-effect relationships better. It reviews what’s been done so far in this area and shows how different approaches compare when tested on different problems. The authors share their findings and suggest where future research should go, emphasizing the potential benefits of using these powerful models to improve our understanding of causality. |
Keywords
» Artificial intelligence » Inference
