Summary of Large Language Models and Causal Inference in Collaboration: a Comprehensive Survey, by Xiaoyu Liu et al.
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey
by Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang
First submitted to arxiv on: 14 Mar 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 survey evaluates and improves Large Language Models (LLMs) in various areas, including understanding and improving reasoning capacity, addressing fairness and safety issues, providing explanations, and handling multimodality. By leveraging LLMs’ strong reasoning capabilities, the review explores the interplay between causal inference frameworks and LLMs from both perspectives, highlighting their collective potential to develop more advanced and equitable artificial intelligence systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs have shown great promise in various NLP domains by capturing complex relationships among variables. This survey focuses on improving these models’ predictive accuracy, fairness, robustness, and explainability by exploring the interplay between causal inference frameworks and LLMs. |
Keywords
» Artificial intelligence » Inference » Nlp
