Graceful Forgetting in Generative Language Models

📰 ArXiv cs.AI

Graceful forgetting in generative language models helps mitigate negative transfer by selectively removing harmful pre-trained knowledge

advanced Published 2 Apr 2026

Action Steps

Identify the pre-trained knowledge that is detrimental to the fine-tuning task
Develop a method to selectively remove or forget this harmful knowledge
Implement the graceful forgetting technique during the fine-tuning process
Evaluate the performance of the model with and without graceful forgetting to measure its effectiveness

Who Needs to Know This

ML researchers and engineers working on fine-tuning pre-trained language models can benefit from this concept to improve their model's performance on downstream tasks

Key Insight

💡 Not all pre-trained knowledge is beneficial, and selectively removing harmful knowledge can improve model performance

Key Takeaways

Graceful forgetting in generative language models helps mitigate negative transfer by selectively removing harmful pre-trained knowledge

Full Article

Title: Graceful Forgetting in Generative Language Models

Abstract:
arXiv:2505.19715v2 Announce Type: replace-cross Abstract: Recently, the pretrain-finetune paradigm has become a cornerstone in various deep learning areas. While in general the pre-trained model would promote both effectiveness and efficiency of downstream tasks fine-tuning, studies have shown that not all knowledge acquired during pre-training is beneficial. Some of the knowledge may actually bring detrimental effects to the fine-tuning tasks, which is also known as negative transfer. To addres

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