Papers by Ruipeng Wang
Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network (2020.emnlp-main)
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| Challenge: | Existing extractive summarization methods focus on balancing salience and redundancy between sentences. |
| Approach: | They propose a hierarchical attentive heterogeneous graph for text summarization that models sentences . they propose to iteratively refine the sentence representations and deliver the labels by message passing . |
| Outcome: | The proposed method outperforms existing extractive summarization methods on large corpus. |
Forget the Token and Pixel: Rethinking Gradient Ascent for Concept Unlearning in Multimodal Generative Models (2025.findings-acl)
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Jiaqi Li, Chuanyi Zhang, Miaozeng Du, Hui Zhang, Yongrui Chen, Qianshan Wei, Junfeng Fang, Ruipeng Wang, Sheng Bi, Guilin Qi
| Challenge: | Gradient Ascent (GA) has emerged as a promising approach for concept unlearning in Multimodal Generative Models (MGMs). |
| Approach: | They propose a novel approach that selectively applies GA to targeted Conceptual Knowledge while preserving Natural Knowledge through Gradient Descent (GD). |
| Outcome: | The proposed approach removes Conceptual Knowledge and inadvertently diminishes Natural Knowledge, resulting in utility degradation. |
Eliciting Medical Reasoning with Knowledge-enhanced Data Synthesis: A Semi-Supervised Reinforcement Learning Approach (2026.findings-acl)
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| Challenge: | Existing methods to enhance medical reasoning lack high-quality data. |
| Approach: | They propose a medical knowledge-enhanced data Synthesis and Semi-supervised Reinforcement learning framework that uses rare disease knowledge to synthesize distribution-controllable reasoning questions. |
| Outcome: | The proposed method outperforms existing methods across ten medical benchmarks and achieves up to 5.93% gain on rare diseases tasks. |
Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization (2022.acl-long)
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| Challenge: | Existing methods to translate sentences to other languages using heuristics are challenging. |
| Approach: | They propose a model that learns hierarchical weights for different sets of labels and applies them to other languages to translate them. |
| Outcome: | The proposed model can translate English datasets to other languages and obtain different sets of labels again using heuristics. |
Deep Differential Amplifier for Extractive Summarization (2021.acl-long)
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| Challenge: | Existing approaches to extract summary from document with a disproportionate ratio of selected and unselected sentences are far from human performance. |
| Approach: | They propose a model that rebalances sentence-level extractive summarization by amplifying the semantic difference between each sentence and all other sentences and applying the residual unit as the second item of the differential amplifier to deepen the architecture. |
| Outcome: | The proposed model performs competitively against state-of-the-art methods on two benchmark datasets. |
Weights-Rotated Preference Optimization for Large Language Models (2025.emnlp-main)
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Chenxu Yang, Ruipeng Jia, Mingyu Zheng, Naibin Gu, Zheng Lin, Siyuan Chen, Weichong Yin, Hua Wu, Weiping Wang
| Challenge: | Existing methods to align large language models with high reward hacking are limited by the complexity of the parameter space and the complexity. |
| Approach: | They propose a weights-rotated preference optimization algorithm that constrains the output layer logits with the KL divergence inherited from DPO and fine-tunes the intermediate hidden states. |
| Outcome: | The proposed algorithm achieves a 3.27-point improvement on AlpacaEval 2 and surpasses the best baseline by 6.2 to 7.5 points on MT-Bench with merely 0.015% of the trainable parameters. |
Neuron-Level Sequential Editing for Large Language Models (2025.acl-long)
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Houcheng Jiang, Junfeng Fang, Tianyu Zhang, Baolong Bi, An Zhang, Ruipeng Wang, Tao Liang, Xiang Wang
| Challenge: | Existing model editing methods focus on single-round editing and often face significant challenges in sequential model editing. |
| Approach: | They propose a model editing method that optimizes the target layer’s hidden states using the model’s original weights to prevent model failure. |
| Outcome: | The proposed method outperforms existing model editing methods and is available on the open-source platform 4open.science. |