Task-Oriented Rank Adaptation for Continual Learning in Text Classification
Continual learning (CL) in text classification faces two critical challenges: catastrophic forgetting and negative transfer across sequential tasks. Parameter-Efficient Fine-Tuning (PEFT) methods such as LoRA enable efficient adaptation by learning low-rank updates of the model parameters. However, these compact representations are normally trained in isolation, limiting their reuse across related tasks. We introduce Task-Oriented Rank Adaptation (TORA), a geometric routing framework that leverages the low-rank structure of LoRA adapters to decide whether to transfer knowledge from the most compatible expert (Boosting) or isolate the new task (Shielding) based on structural similarity. Ev...
| Authors | Rey Sanchez Lopez, Eduardo Morales Manzanares, Hugo Jair Escalante |
|---|---|
| Primary category | cs.CL |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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