Evaluating and Inducing Dialectal Robustness in Large Language Models

Overview

Explore how language models perform across dialects and variants, with a focus on DialUp, a method to induce robustness to dialect continua in machine translation models, including for unseen dialects.

Syllabus

• Course Introduction and Overview
Course objectives and outcomes
Importance of dialectal robustness in language models
• Basics of Dialectal Variation
Definitions of dialects and dialect continua
Examples and challenges posed by dialectal differences in NLP
• Language Models and Dialectal Robustness
Overview of large language models
Limitations of current models in handling dialects
• Evaluation Techniques for Dialectal Robustness
Metric evaluation for dialect handling
Benchmark datasets featuring dialectal variation
Qualitative vs. quantitative assessment
• Introduction to DialUp Method
Concept and principles behind DialUp
Case studies demonstrating DialUp's effectiveness
• Implementing DialUp for Machine Translation
Technical overview of integrating DialUp in models
Training and validation on multiple dialects
• Addressing Unseen Dialects
Strategies for generalization across unseen dialectial data
Transfer learning and domain adaptation techniques
• Hands-On Workshop
Practical exercises on evaluating dialectal robustness
Implementing DialUp on provided text datasets
• Ethical Considerations
Bias and fairness in dialectal variations
Cultural sensitivity and informed data usage
• Final Project
Design and implement a model to enhance dialectal robustness using DialUp
Present findings and performance results
• Course Summary and Future Directions
Recap of key learnings
Emerging trends in dialectal robustness and NLP

Subjects

Computer Science