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Found 4 papers, 3 papers with code
Incremental Sequence Labeling: A Tale of Two Shifts
As for the E2O problem, we use knowledge distillation to maintain the model's discriminative ability for old entities.
Concept-1K: A Novel Benchmark for Instance Incremental Learning
However, existing IL scenarios and datasets are unqualified for assessing forgetting in PLMs, giving an illusion that PLMs do not suffer from catastrophic forgetting.
Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models
Most assume that catastrophic forgetting is the biggest obstacle to achieving superior IL performance and propose various techniques to overcome this issue.
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference
Intriguingly, the unified objective can be seen as the sum of the vanilla fine-tuning objective, which learns new knowledge from target data, and the causal objective, which preserves old knowledge from PLMs.
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