Prompting for Evaluation of Free-Text Explanations - Part 2

Overview

Dive into advanced techniques for evaluating free-text explanations in AI prompting, focusing on methodologies and practical applications for data science analysis.

Syllabus

• Introduction to Advanced Evaluation of Free-Text Explanations
Overview of evaluation methodologies
Importance in AI and data science
• Review of Basic Concepts from Part 1
Recap of fundamental concepts
Key takeaways and their relevance
• Advanced Evaluation Metrics
Precision, recall, and F1-score in explanation evaluation
Semantic similarity measures
Human-centric metrics: fluency, relevance, and persuasiveness
• Methodologies for Evaluation
Qualitative vs. Quantitative approaches
Crowdsourcing for human evaluation
Automated tools and frameworks
• Designing Evaluation Frameworks
Creating a rubric for explanation quality
Balancing objective and subjective measures
Case studies of existing frameworks
• Practical Applications in Data Science
Real-world data science problems
Integrating evaluation into the AI development lifecycle
Continuous improvement through iterative feedback
• Hands-On Workshops
Case study analysis
Applying metrics to sample datasets
Group projects on designing an evaluation framework
• Challenges and Future Directions
Balancing explainability with performance
Emerging trends in AI explanation evaluation
Ethical considerations in AI prompting
• Conclusion and Next Steps
Summary of key learning points
Resources for further study
Preparing for future advancements in AI prompting evaluation

Subjects

Data Science