Summary of Prompting Strategies For Enabling Large Language Models to Infer Causation From Correlation, by Eleni Sgouritsa et al.
Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation
by Eleni Sgouritsa, Virginia Aglietti, Yee Whye Teh, Arnaud Doucet, Arthur Gretton, Silvia Chiappa
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 focuses on enhancing the causal reasoning abilities of Large Language Models (LLMs) by introducing a novel prompting strategy for establishing causal relationships based on correlation information. The proposed approach, called PC-SubQ, breaks down the original task into fixed subquestions, mirroring the steps of the PC algorithm, a formal causal discovery method. By sequentially prompting the LLM with these subquestions, each augmented with the answer to the previous one(s), our strategy guides the model to follow the algorithmic steps and improve its performance on the challenging Corr2Cause benchmark. We demonstrate significant performance gains across five LLMs when compared to baseline strategies, while robustly handling perturbations in causal queries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how Large Language Models (LLMs) can be improved at understanding cause-and-effect relationships. The researchers developed a new way of asking the model questions that helps it follow a specific algorithm for finding causes. By breaking down the task into smaller, manageable pieces and providing the answer to each question as the next one is asked, our approach boosts the LLM’s performance on a challenging test. We tested five different models and saw significant improvements when using our method. This approach also remains effective even when the questions are slightly changed. |
Keywords
» Artificial intelligence » Prompting
