Summary of Large Language Models Are Effective Priors For Causal Graph Discovery, by Victor-alexandru Darvariu et al.
Large Language Models are Effective Priors for Causal Graph Discovery
by Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The proposed set of metrics evaluates Large Language Models’ (LLMs) judgments for causal graph discovery independently of downstream algorithms. The study also explores prompting designs that allow LLMs to specify priors about the structure of the causal graph. The integration of LLM priors in graph discovery algorithms improves performance on common-sense benchmarks, particularly when assessing edge directionality. This work highlights both the potential and limitations of using LLMs for causal structure discovery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs can help discover causal structures from observations by providing prior knowledge. A set of metrics assesses their judgments independently of downstream algorithms. The study looks at how to ask LLMs questions that help them specify what the causal graph should look like. By combining LLM inputs with graph discovery algorithms, performance improves on common-sense benchmarks. This is especially true when trying to figure out which edges point in which direction. |
Keywords
» Artificial intelligence » Prompting
