GrounDial: Human-norm Grounded Safe Dialog Response Generation
Project Overview
The document explores the innovative application of generative AI in education, particularly through the introduction of GrounDial, a conversational AI framework that enhances the safety and contextual relevance of responses in educational settings. GrounDial operates without requiring additional fine-tuning of large language models, utilizing human-norm-guided decoding and in-context learning to align responses with established social norms. This methodology significantly improves safety and agreement scores in dialogue systems, making it a promising tool for educational interactions. By providing contextually appropriate and norm-compliant responses, GrounDial aims to facilitate more effective communication between AI systems and learners, thereby enhancing the overall educational experience. The findings underscore the potential of generative AI to transform educational environments, emphasizing the importance of safety and relevance in fostering productive learning outcomes. Overall, the document highlights how advancements in conversational AI frameworks like GrounDial can lead to more engaging and secure educational experiences, paving the way for broader applications of AI technologies in teaching and learning processes.
Key Applications
GrounDial
Context: Conversational AI systems for end-users requiring safe dialogue responses.
Implementation: Implemented a hybrid approach combining in-context learning and human-norm-guided decoding to generate responses.
Outcomes: Improved safety and relevance of generated responses, achieving higher safety and agreement scores compared to traditional methods.
Challenges: Occasional generation of incorrect words and some unsafe responses due to limitations in language modeling capacity.
Implementation Barriers
Technical Barrier
The language modeling capacity of existing systems may not be sufficient to generate consistently safe responses.
Proposed Solutions: Further research into improving model capabilities and introducing advanced reward designs for decoding.
Cost Barrier
Traditional methods require significant costs for collecting safe dialogues and fine-tuning large-scale language models.
Proposed Solutions: GrounDial eliminates the need for additional fine-tuning, reducing costs associated with implementing safe dialogue systems.
Project Team
Siwon Kim
Researcher
Shuyang Dai
Researcher
Mohammad Kachuee
Researcher
Shayan Ray
Researcher
Tara Taghavi
Researcher
Sungroh Yoon
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Siwon Kim, Shuyang Dai, Mohammad Kachuee, Shayan Ray, Tara Taghavi, Sungroh Yoon
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