Summary of Hypothesizing Missing Causal Variables with Llms, by Ivaxi Sheth et al.
Hypothesizing Missing Causal Variables with LLMs
by Ivaxi Sheth, Sahar Abdelnabi, Mario Fritz
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed task aims to develop a novel approach for scientific discovery by generating hypotheses about missing variables in partial causal graphs. The task is motivated by the scientific discovery process, which involves hypothesis generation, experimental design, data evaluation, and iterative assumption refinement. To achieve this, the authors formulate a benchmark with varying difficulty levels and knowledge assumptions about the causal graph. They then evaluate open-source and closed Large Language Models (LLMs) on this testbed, demonstrating their ability to hypothesize mediation variables between causes and effects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are constantly trying to figure out how things work by generating hypotheses, testing them, and refining their ideas. This process can be time-consuming and requires a lot of expertise. To help with this, researchers have developed a new way to use computers to generate hypotheses about missing information in complex networks. They created a test to see how well these computer models could do this task and found that some models were better than others at guessing the relationships between causes and effects. |
