Papers by Yongcheng Jing
Erasing Without Remembering: Implicit Knowledge Forgetting in Large Language Models (2026.acl-long)
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| Challenge: | a new method for unlearning large language models is proposed to improve the performance of large language model models. |
| Approach: | They propose a probability perturbation-based unlearning paradigm that allows models to forget implicit knowledge in large language models with a focus on generalisation. |
| Outcome: | The proposed model improves unlearning vanilla target data while forgetting implicit knowledge. |
Dynamic Parallel Tree Search for Efficient LLM Reasoning (2025.acl-long)
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Yifu Ding, Wentao Jiang, Shunyu Liu, Yongcheng Jing, Jinyang Guo, Yingjie Wang, Jing Zhang, Zengmao Wang, Ziwei Liu, Bo Du, Xianglong Liu, Dacheng Tao
| Challenge: | Recent methods focus on search accuracy while overlooking computational efficiency. |
| Approach: | They propose a parallelism framework that dynamically optimizes reasoning path in inference. |
| Outcome: | The proposed framework improves efficiency by 2-4 on average while maintaining or even surpassing existing reasoning algorithms in accuracy. |
Distillation Traps and Guards: A Calibration Knob for LLM Distillability (2026.acl-long)
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| Challenge: | Knowledge distillation (KD) transfers capabilities from large language models (LLMs) to smaller students, yet it can fail unpredictably and also underpins model leakage risks. |
| Approach: | They propose a method that allows teachers to control their distillability via reinforcement fine-tuning (RFT) they propose to use tail noise, off-policy instability, and the teacher–student gap to improve KD. |
| Outcome: | The proposed method outperforms SFT and KD baselines and can be used to protect teachers and students from bottlenecks. |