Papers with gradient-based fine-tuning
Tree Prompting: Efficient Task Adaptation without Fine-Tuning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Pretrained language models (LMs) are the main interface for applying them to new tasks, but their large size makes them difficult to fine-tune with gradients for specific downstream tasks. |
| Approach: | They propose to use training data to form a decision tree based on prompt-LM calls, with each prompt determined by the outcomes of previous calls. |
| Outcome: | The proposed method improves accuracy over competing methods and is competitive with fine-tuning. |
Model Unlearning via Sparse Autoencoder Subspace Guided Projections (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing unlearning strategies lack interpretability or fail to provide robust defense against adversarial prompts. |
| Approach: | They propose a framework that leverages SAE features to drive targeted updates in the model’s parameter space. |
| Outcome: | The proposed framework reduces harmful knowledge accuracy by 3.22% compared to baselines and improves adversarial robustness under jailbreak prompts. |