Across
- 2. Decomposition used by LoRA
- 4. LoRA introduces trainable rank decomposition
- 6. LoRA improves this efficiency
- 9. LoRA preserves this
- 12. Efficient adaptation method
- 13. LoRA's seamless feature
- 15. LoRA offers a practical one
- 16. Further exploration of LoRA
Down
- 1. LoRA outperforms this
- 3. Fine-Tuning's drawbacks
- 5. Consistency across these
- 7. Analyzing this reveals insights
- 8. LoRA can be combined with these improvements
- 10. LoRA's versatile feature
- 11. Adaptation matrix impacts this
- 14. LoRA decreases requirements