Papers by Wenbin Liu

Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering (2022.coling-1)

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Jianguo Mao, Jiyuan Zhang, Zengfeng Zeng, Weihua Peng, Wenbin Jiang, Xiangdong Wang, Hong Liu, Yajuan Lyu

Challenge:	Existing models of biomedical question answering are limited in their ability to predict answers . a new model improves the performance of existing models, but the code will be released after the paper is published.
Approach:	They propose a hierarchical representation-based dynamic reasoning network to solve biomedical problems.
Outcome:	The proposed model significantly improves on three mainstream biomedical datasets . the code will be released after the paper is published .

Mitigating the Alignment Tax of RLHF (2024.emnlp-main)

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Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang

Language Models

Challenge:	Large Language Models (LLMs) acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, which is also known as the alignment tax.
Approach:	They propose to use a model averaging technique to find the most powerful alignment-forging Pareto front among RLHF algorithms.
Outcome:	The proposed method achieves the strongest alignment-forging Pareto front among competing methods.

BanditMTL: Bandit-based Multi-task Learning for Text Classification (2021.acl-long)

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Yuren Mao, Zekai Wang, Weiwei Liu, Xuemin Lin, Wenbin Hu

Responsible & Trustworthy NLP

Challenge:	Existing methods to regularize task variance are unexplored in multi-task text classification.
Approach:	They propose a multi-task learning method based on adversarial multi-armed bandit to regularize the task variance by means of a mirror gradient ascent-descent algorithm.
Outcome:	The proposed method achieves state-of-the-art in multi-task text classification.

Characterizing the Impacts of Instances on Robustness (2023.findings-acl)

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Rui Zheng, Zhiheng Xi, Qin Liu, Wenbin Lai, Tao Gui, Qi Zhang, Xuanjing Huang, Jin Ma, Ying Shan, Weifeng Ge

Robustness in NLP

Challenge:	Existing defense approaches focus on developing new model structures or training algorithms, but they do little to tap the potential of training instances.
Approach:	They propose a method that can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training.
Outcome:	The proposed method can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training.

Dynamic Multistep Reasoning based on Video Scene Graph for Video Question Answering (2022.naacl-main)

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Jianguo Mao, Wenbin Jiang, Xiangdong Wang, Zhifan Feng, Yajuan Lyu, Hong Liu, Yong Zhu

Multimodality

Challenge:	Existing video QA models lack the capacity for deep video understanding and flexible multistep reasoning.
Approach:	They propose a video question answering model which performs dynamic multistep reasoning between questions and videos.
Outcome:	The proposed model improves on three widely used video QA datasets and displays better interpretability by backtracing along with the attention mechanisms to the video scene graphs.

Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules (2023.findings-emnlp)

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Chaojun Xiao, Yuqi Luo, Wenbin Zhang, Pengle Zhang, Xu Han, Yankai Lin, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou

Semantic Text Processing

Challenge:	Large language models (LLMs) have been successful on NLP tasks but require huge parameter sizes and computational resources.
Approach:	They propose a parameter-efficient acceleration method that enhances computational efficiency through plug-and-play compression plugins.
Outcome:	The proposed method saves 53% computational costs using only 0.9% additional parameters with a performance drop of less than 2%.

Fine-Grained Legal Argument-Pair Extraction via Coarse-Grained Pre-training (2024.lrec-main)

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Chaojun Xiao, Yutao Sun, Yuan Yao, Xu Han, Wenbin Zhang, Zhiyuan Liu, Maosong Sun

Language Models

Challenge:	Current methods conceptualize LAE as a supervised sentence-pair classification problem and necessitate extensive manual annotations.
Approach:	They propose a model that focuses on fine-grained alignment of argument pairs building upon coarse-grain complaint-defense pairs.
Outcome:	The proposed model outperforms baseline models by 3.7 and 2.4 points on average.

Token-Level Policy Optimization: Linking Group-Level Rewards to Token-Level Aggregation via sequence-level likelihood (2026.acl-long)

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Xingyu Lin, Yilin Wen, Du Su, En Wang, Wenbin Liu, Zhonghou Lv, Jinchang Hou, Chenfu Bao

Reasoning

Challenge:	Group Relative Policy Optimization (GRPO) has significantly advanced the reasoning ability of large language models (LLMs).
Approach:	They propose a token-level framework that leverages sequence-level likelihood to link group-level rewards with individual tokens via token- level aggregation and introduces a KL-Divergence mask constraint that targets tokens with positive advantages and decreasing entropy to mitigate abrupt policy updates.
Outcome:	Experiments show that TEPO achieves state-of-the-art performance on mathematical reasoning benchmarks and reduces convergence time by 50% compared with GRPO/DAPO.

Machine Reading Comprehension Using Structural Knowledge Graph-aware Network (D19-1)

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Delai Qiu, Yuanzhe Zhang, Xinwei Feng, Xiangwen Liao, Wenbin Jiang, Yajuan Lyu, Kang Liu, Jun Zhao

Multimodality

Challenge:	Recent large-scale datasets specify that external knowledge is required to answer questions.
Approach:	They propose a model that leverages external knowledge to construct sub-graphs for entities in machine comprehension context.
Outcome:	The proposed model achieves state-of-the-art performance on the ReCoRD dataset.

DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs (2025.findings-emnlp)

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Xiabin Zhou, Wenbin Wang, Minyan Zeng, Jiaxian Guo, Xuebo Liu, Li Shen, Min Zhang, Liang Ding

Challenge:	Existing KV cache compression methods enforce a fixed pattern, neglecting task-specific characteristics, which hampers the effective retention of essential information while discarding less important tokens.
Approach:	They propose a Task-Aware KV cache mechanism that dynamically adjusts the KV caching size across different layers based on the characteristics of the tasks.
Outcome:	The proposed method surpasses state-of-the-art methods by 11% on the LongBench dataset even under extreme compression (0.9%)

Explainable Question Answering based on Semantic Graph by Global Differentiable Learning and Dynamic Adaptive Reasoning (2022.emnlp-main)

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Jianguo Mao, Wenbin Jiang, Xiangdong Wang, Hong Liu, Yu Xia, Yajuan Lyu, QiaoQiao She

Explainability & Interpretability in NLP

Challenge:	Existing models for multi-hop Question Answering have improved the implicit reasoning ability, but the black box nature of pure neural networks has hindered the construction of explainable intelligent systems.
Approach:	They propose a global differentiation strategy to explore optimal reasoning paths from latent probability space and a Dynamic Adaptive Reasoner to enhance generalization of unseen questions.
Outcome:	The proposed method achieves 17% improvements in F1-score against BreakRC and shows better interpretability.