Summary of Ocdb: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Framework, by Wei Zhou et al.
OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Framework
by Wei Zhou, Hong Huang, Guowen Zhang, Ruize Shi, Kehan Yin, Yuanyuan Lin, Bang Liu
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 addresses the limitations of large language models (LLMs) in high-stakes fields due to issues with interpretability and trustworthiness. The authors propose a flexible evaluation framework that focuses on metrics for evaluating differences in causal structures and causal effects, which are crucial for improving the transparency of LLMs. They introduce the Open Causal Discovery Benchmark (OCDB), based on real data, to promote fair comparisons and drive optimization of algorithms. The proposed framework also accounts for undirected edges, enabling fair comparisons between Directed Acyclic Graphs (DAGs) and Completed Partially Directed Acyclic Graphs (CPDAGs). Experimental results demonstrate significant shortcomings in existing algorithms’ generalization capabilities on real data, highlighting the potential for performance improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper wants to make sure that big language models are reliable and easy to understand. Right now, these models aren’t very good at showing us why they’re making certain predictions or decisions. The authors want to fix this by creating a way to test how well these models do when they’re used in real-life situations. They also want to compare different models fairly, so we can see which ones are the best. The results show that some models aren’t very good at working in real-life scenarios, and this is important for making sure we can trust these models. |
Keywords
» Artificial intelligence » Generalization » Optimization
