Generative Adversarial Imitation Learning for Empathy-based AI
Project Overview
The document examines the application of generative AI in education through the development of an empathy-based conversational AI system that employs Generative Adversarial Imitation Learning (GAIL) and the GPT-2 language model. This innovative system is designed to generate context-aware and empathetic responses in dialogues, addressing the critical challenge of enabling AI to understand and interact with human emotions effectively. By utilizing deep reinforcement learning, the model is fine-tuned based on expert empathetic dialogues, leading to significant improvements in response quality compared to baseline models. The findings suggest that such AI systems can enhance educational interactions by providing more personalized and emotionally intelligent support to learners, thereby fostering a more engaging and supportive learning environment. The research underscores the potential of generative AI to transform educational experiences by integrating emotional understanding into AI-driven communication tools, ultimately aiming to improve learner outcomes and satisfaction.
Key Applications
Empathy-based conversational AI using GAIL
Context: Conversational AI for mental health support, targeting individuals seeking empathetic interactions.
Implementation: The model was implemented by fine-tuning the GPT-2 language model using expert empathetic dialogues collected from various datasets.
Outcomes: Achieved significant improvements in generating empathetic responses, as evidenced by lower perplexity and BLEU scores compared to baseline models.
Challenges: Challenges include generating fake experiences and handling biases present in the training data.
Implementation Barriers
Technical
Difficulty in defining and measuring empathy in AI responses.
Proposed Solutions: Utilizing expert trajectories of empathetic dialogues to guide the AI's learning process.
Ethical
Risk of generating inappropriate or harmful content due to biases in training data.
Proposed Solutions: Implementing rigorous testing and validation practices before public deployment.
Project Team
Pratyush Muthukumar
Researcher
Karishma Muthukumar
Researcher
Deepan Muthirayan
Researcher
Pramod Khargonekar
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Pratyush Muthukumar, Karishma Muthukumar, Deepan Muthirayan, Pramod Khargonekar
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