Summary of Realtcd: Temporal Causal Discovery From Interventional Data with Large Language Model, by Peiwen Li et al.
RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
by Peiwen Li, Xin Wang, Zeyang Zhang, Yuan Meng, Fang Shen, Yue Li, Jialong Wang, Yang Li, Wenweu Zhu
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 The paper proposes a novel approach for temporal causal discovery in industrial scenarios, focusing on real-world systems with complex textual information. The authors aim to tackle two critical challenges: discovering causal relationships without interventional targets and leveraging textual information to improve discovery. They introduce the RealTCD framework, combining a score-based temporal causal discovery method with Large Language Models (LLMs) for meta-initialization. This enables the extraction of domain knowledge from system texts to boost discovery quality. The authors demonstrate the effectiveness of their proposed approach through extensive experiments on simulation and real-world datasets, outperforming existing baselines in discovering temporal causal structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how artificial intelligence can help with information technology operations. It’s about finding causes and effects between different things that happen over time. Right now, there are some methods that work well for small, fake data sets, but they don’t work as well when dealing with real-world systems, which have lots of complex text data. The authors want to solve this problem by developing a new approach that can handle real-world scenarios and learn from the text data. They propose an approach called RealTCD, which uses both old methods and new language models to improve discovery. The results show that their approach works better than existing methods for finding causal relationships. |
