Papers by Anda Cheng
Mitigating Catastrophic Forgetting in Large Language Models with Forgetting-aware Pruning (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) have shown impressive capabilities in various downstream tasks but typically face Catastrophic Forgetting (CF) during fine-tuning. |
| Approach: | They propose a pruning-based approach to balance CF and downstream task performance by integrating the ratio of the task vector to pre-trained model parameters into the pruning criteria. |
| Outcome: | The proposed pruning-based approach limits CF to just 0.25% while maintaining 99.67% accuracy on downstream tasks. |
A Fully Probabilistic Perspective on Large Language Model Unlearning: Evaluation and Optimization (2025.emnlp-main)
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| Challenge: | Large Language Model Unlearning (LLMU) is a promising way to remove private or sensitive information from large language models. |
| Approach: | They propose a Fully Probabilistic Evaluation framework that incorporates input and output distributions in LLMU evaluation. |
| Outcome: | The proposed framework improves unlearning effectiveness by 50.1% and robustness by 37.2% on Llama-2-7B. |
DPF-CM: A Data Processing Framework with Privacy-Preserving Vector Databases for Chinese Medical LLMs Training and Deployment (2025.findings-emnlp)
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| Challenge: | a new framework for training data processing for Chinese medical language models is proposed . experimental results show that the framework significantly improves model accuracy . |
| Approach: | They propose a data processing framework for Chinese medical language models training and deployment . the framework is based on a question-oriented model training strategy and privacy preservation . |
| Outcome: | The proposed framework significantly improves model accuracy and reduces privacy leakage by 27%. |