Human-Centric Trustworthy AI/NLP
Project Description
- Information trustworthiness and foundation model knowledge boundary awareness
- Model self-play, self-correction, and rethinking from theoretical and empirical perspectives
- Model self-play, self-correction, and rethinking from theoretical and empirical perspectives
Supervisor
FUNG, May
Quota
2
Course type
UROP1100
UROP3100
Applicant's Roles
- Research and Analysis: Conduct literature reviews and analyze existing methodologies in human-centric trustworthy AI and NLP reasoning.
- Data Collection and Preprocessing: Gather and preprocess datasets relevant to the project, ensuring data quality and integrity.
- Algorithm Development: Assist in developing and implementing algorithms for trustworthy AI and NLP reasoning.
- Experimentation: Design and execute experiments to test and validate the developed models and algorithms.
- Documentation and Reporting: Document research findings, methodologies, and results, and prepare reports and presentations for project meetings.
- Collaboration: Work closely with team members, including faculty advisors and other researchers, to ensure project goals are met.
- Data Collection and Preprocessing: Gather and preprocess datasets relevant to the project, ensuring data quality and integrity.
- Algorithm Development: Assist in developing and implementing algorithms for trustworthy AI and NLP reasoning.
- Experimentation: Design and execute experiments to test and validate the developed models and algorithms.
- Documentation and Reporting: Document research findings, methodologies, and results, and prepare reports and presentations for project meetings.
- Collaboration: Work closely with team members, including faculty advisors and other researchers, to ensure project goals are met.
Applicant's Learning Objectives
- Understanding Trustworthy AI: Gain a deep understanding of the principles and challenges associated with developing trustworthy AI systems.
- NLP Techniques: Learn advanced NLP techniques and how they can be applied to enhance AI reasoning.
- Research Skills: Develop strong research skills, including literature review, data analysis, and experimental design.
- Technical Proficiency: Improve technical skills in programming, algorithm development, and data preprocessing.
- Communication Skills: Enhance ability to document and present research findings clearly and effectively.
- Collaboration and Teamwork: Experience working in a collaborative research environment, learning to communicate and coordinate with team members.
- NLP Techniques: Learn advanced NLP techniques and how they can be applied to enhance AI reasoning.
- Research Skills: Develop strong research skills, including literature review, data analysis, and experimental design.
- Technical Proficiency: Improve technical skills in programming, algorithm development, and data preprocessing.
- Communication Skills: Enhance ability to document and present research findings clearly and effectively.
- Collaboration and Teamwork: Experience working in a collaborative research environment, learning to communicate and coordinate with team members.
Complexity of the project
Challenging