Summary of Using Llms For Explaining Sets Of Counterfactual Examples to Final Users, by Arturo Fredes and Jordi Vitria
Using LLMs for Explaining Sets of Counterfactual Examples to Final Users
by Arturo Fredes, Jordi Vitria
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The proposed novel multi-step pipeline uses counterfactuals to generate natural language explanations for tabular data classifiers using Large Language Models (LLMs). By manipulating features and creating hypothetical scenarios, this approach enables end-users to understand how to change their situation. The pipeline guides the LLM through smaller tasks that mimic human reasoning when explaining a decision based on counterfactual cases. Experiments were conducted using a public dataset with promising results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Causality is important in Explainable AI because it helps understand true cause-and-effect relationships between variables, not just correlations. This paper proposes a way to explain decisions made by predictive models. It uses “what if” scenarios called counterfactuals to show how changing certain factors can change the outcome. The goal is to make this information easy to understand for people who aren’t experts in data analysis. |
