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Starts 8 June 2025 00:14
Ends 8 June 2025
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The Frontier between Retrieval-augmented and Long-context Language Models
Explore the evolving boundary between retrieval-augmented and long-context language models with Princeton researcher Danqi Chen in this technical talk from the Simons Institute.
Simons Institute
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Simons Institute
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Overview
Explore the evolving boundary between retrieval-augmented and long-context language models with Princeton researcher Danqi Chen in this technical talk from the Simons Institute.
Syllabus
- Introduction to Retrieval-augmented Models
- Overview of Long-context Language Models
- Comparative Analysis: Retrieval-Augmented vs. Long-Context Models
- Integration Techniques
- Recent Advances and Research Directions
- Practical Considerations
- Conclusion
- Q&A Session
Definition and basic concepts
Key differences from traditional models
Applications and use cases
Definition and basic concepts
Advantages over short-context models
Notable examples and their impact
Strengths and weaknesses
Performance metrics and benchmarks
Case studies
Hybrid models combining retrieval and long-context
Techniques for optimizing performance
Real-world applications
Cutting-edge research by Danqi Chen and other leaders
Trends and future developments
Challenges in implementation
Resource management and scalability
Ethical implications and responsible use
Key takeaways
Open questions and areas for further exploration
Interactive discussion with participants
Addressing specific questions related to the field
Subjects
Computer Science
