Using LLMs for Explaining Sets of Counterfactual Examples to Final Users
Project Overview
The document highlights the transformative role of generative AI, particularly large language models (LLMs), in education by focusing on their ability to enhance understanding through explainable AI (XAI). It outlines how LLMs can generate clear explanations of counterfactual examples in machine learning, thus aiding educators and learners in grasping complex concepts and decision-making processes. The proposed methodology dissects the explanation process into manageable components, allowing users to better understand the factors that influence educational outcomes. This increased transparency not only aids learners in making informed decisions but also empowers educators to tailor their approaches for improved effectiveness. Overall, the findings suggest that integrating generative AI in educational contexts can significantly enhance learning experiences, foster deeper understanding, and support effective decision-making among both students and educators.
Key Applications
Using LLMs to generate natural language explanations from counterfactual examples.
Context: Education and decision-making, targeting individuals seeking to understand why they received a specific outcome from an automated system.
Implementation: A multi-step pipeline that uses counterfactual examples to guide LLMs in generating explanations that mimic human reasoning.
Outcomes: Users receive clear, actionable explanations about how to change their outcomes, improving understanding and trust in AI decision-making.
Challenges: Users may struggle to interpret multiple counterfactuals without prior data analytics training, and ensuring the LLM generates coherent and relevant explanations can be complex.
Implementation Barriers
User Understanding
End-users may not understand counterfactuals or the underlying causal relationships without training in data analysis.
Proposed Solutions: Develop user-friendly interfaces and educational materials to help users grasp the concepts of counterfactual reasoning and data analytics.
Model Limitations
LLMs may struggle with tasks requiring complex reasoning or planning, potentially resulting in less effective explanations.
Proposed Solutions: Break down complex tasks into smaller, manageable components to facilitate better processing by the LLM.
Project Team
Arturo Fredes
Researcher
Jordi Vitria
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Arturo Fredes, Jordi Vitria
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai