Papers by Yongcheng Wang
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. |
Refine, Align, and Aggregate: Multi-view Linguistic Features Enhancement for Aspect Sentiment Triplet Extraction (2024.findings-acl)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplets of aspect terms, their associated sentiment and opinion terms. |
| Approach: | They propose to use multi-view linguistic features enhancement to explore the prior indication effect in the “Refine, Align, and Aggregate” learning process to enhance aspect-opinion relations. |
| Outcome: | The proposed model achieves state-of-the-art on several benchmark datasets and is robust to state- of-the art constraints. |
Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction (2025.coling-main)
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| Challenge: | Existing table-filling methods decompose the ASQP task into subtasks without considering the association between sentiment elements. |
| Approach: | They propose a simple yet effective Unified Grid Tagging Scheme to extract sentiment quadruplets in one shot . they leverage syntactic dependency tree and AMR graph to enrich association between sentiment elements . |
| Outcome: | The proposed model extracts all sentiment elements in quads for a given review to explain the reason for the sentiment. |
YouMakeup: A Large-Scale Domain-Specific Multimodal Dataset for Fine-Grained Semantic Comprehension (D19-1)
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| Challenge: | Multimodal semantic comprehension has attracted increasing research interest recently such as visual question answering and caption generation. |
| Approach: | They propose to use a large-scale multimodal instructional video dataset to support fine-grained comprehension research in specific domain. |
| Outcome: | The proposed dataset contains 2,800 videos from YouTube, spanning more than 420 hours in total. |