Concept Induction using LLMs: a user experiment for assessment
Project Overview
The document investigates the application of Large Language Models (LLMs), specifically GPT-4, in the realm of education, focusing on their role in concept induction within Explainable AI (XAI). It highlights a comparative analysis of explanations produced by GPT-4, human experts, and the Efficient Concept Induction from Individuals (ECII) system. The findings reveal that while human-generated explanations are favored for their clarity and insight, GPT-4's explanations are more comprehensible than those generated by ECII, suggesting that LLMs can significantly improve the understandability of AI systems. This indicates a promising avenue for enhancing educational tools and resources, where generative AI can facilitate better learning outcomes by providing clearer and more accessible explanations of complex concepts. Overall, the study underscores the potential of generative AI not only to assist in educational contexts but also to contribute to the broader goal of making AI systems more explainable and user-friendly.
Key Applications
Concept Induction using GPT-4
Context: Educational context for understanding AI and its explainability; target audience includes researchers and students in AI and computer science.
Implementation: Utilized GPT-4 via OpenAI API to generate explanations for image classifications based on minimal textual information from an existing dataset.
Outcomes: GPT-4 generated explanations were found to be more comprehensible than those produced by ECII, although still less effective than human-generated explanations.
Challenges: LLM-generated explanations can sometimes include irrelevant or less accurate concepts due to their broad training data and prompting techniques.
Implementation Barriers
Technical
LLMs can generate irrelevant concepts due to uncontrolled nature and reliance on annotated object information.
Proposed Solutions: Improvement of prompting techniques with varied hyper-parameters and integration of vision-based models for better object identification.
Project Team
Adrita Barua
Researcher
Cara Widmer
Researcher
Pascal Hitzler
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Adrita Barua, Cara Widmer, Pascal Hitzler
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