Understand it before memorizing it.
Each token dynamically decides which other tokens matter for its representation.
Where this model earns its place
Language
Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.
Multimodal sequences
Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.
Long-range dependencies
Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.
Trade-offs matter more than popularity.
Strengths
✓ Parallel training
✓ Flexible context modeling
Limitations
△ Memory grows with context
△ Large data/compute needs
Metrics to watch
Before it reaches real users
- 01
Control context size
Document the assumption and instrument the condition so regressions can be detected.
- 02
Cache wisely
Document the assumption and instrument the condition so regressions can be detected.
- 03
Evaluate hallucination/grounding
Document the assumption and instrument the condition so regressions can be detected.