Papers by Zhuan Shi
REVIVING YOUR MNEME: Predicting The Side Effects of LLM Unlearning and Fine-Tuning via Sparse Model Diffing (2025.emnlp-main)
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| Challenge: | Existing evaluation methods assess performance after LLMs are fine-tuned or unlearned to adapt to new tasks or eliminate undesirable behaviors. |
| Approach: | They propose a framework for identifying unintended side effects using sparse model diffing. |
| Outcome: | The proposed framework can detect unintended side effects without fine-tuning data . it achieves 95% accuracy in predicting side effects, aligning with known benchmarks . |
Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs (2026.eacl-long)
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Alireza Dehghanpour Farashah, Aditi Khandelwal, Marylou Fauchard, Zhuan Shi, Negar Rostamzadeh, Golnoosh Farnadi
| Challenge: | Existing studies on unlearning in multilingual large language models focus on monolingual settings, typically English. |
| Approach: | They propose to use a multilingual data and concept unlearning model to investigate the problem . they extend benchmarks for factual knowledge and stereotypes into ten languages . |
| Outcome: | The proposed model is able to unlearning in 10 languages across five languages and resource levels. |