Symbolic Reasoning about Large Language Models

Overview

Explore how symbolic reasoning algorithms can be integrated into language models for controllable text generation and alignment, challenging the token-based reasoning paradigm.

Syllabus

• Introduction to Symbolic Reasoning and Large Language Models
Overview of Symbolic AI
Basics of Large Language Models (LLMs)
Why Combine Symbolic Reasoning with LLMs?
• Theoretical Foundations
Symbolic Logic and Semantic Representation
Formal Methods in AI and NLP
Challenges in Token-based Reasoning
• Symbolic Reasoning Algorithms
Rule-based Systems
Ontologies and Knowledge Graphs
Constraint Satisfaction Problems
• Integrating Symbolic Methods with Language Models
Hybrid Models for Controllable Text Generation
Case Studies: Successful Integrations
Challenges and Limitations
• Controllable Text Generation
Techniques for Control in Generation
Leveraging Symbolic Components for Consistency
Applications and Use Cases
• Alignment of LLMs with Human Goals
Defining and Measuring Alignment
Symbolic Techniques for Enhanced Alignment
Ethical Considerations
• Practical Implementation
Tools and Frameworks for Hybrid Systems
Step-by-Step Integration Process
Hands-on Activities and Laboratory Sessions
• Advanced Topics and Research Directions
The Future of Symbolic Reasoning in AI
Emerging Paradigms and Technologies
Current Research and Open Challenges
• Summary and Conclusion
Key Takeaways
Discussion on the Role of Symbolic Reasoning in Future AI
• Final Project / Capstone
Project Proposal and Guidelines
Integration of Symbolic Reasoning in a Selected LLM Application
Presentation and Peer Review
• Additional Resources
Recommended Readings
Online Courses and Workshops
Conferences and Journals in the Field

Subjects

Computer Science