Papers by Weiping Liu
SGG-R 3: From Next-Token Prediction to End-to-End Unbiased Scene Graph Generation (2026.findings-acl)
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| Challenge: | Existing methods for scene graph generation lack task-specific structured reasoning and sparse, long-tailed relation distributions. |
| Approach: | They propose a structured reasoning framework that integrates task-specific Chain-of-Thought and reinforcement learning with group sequence policy optimization to achieve unbiased scene graph generation. |
| Outcome: | The proposed framework achieves superior performance on two benchmarks. |
Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning (2024.findings-acl)
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| Challenge: | Existing methods for fine-tuning large language models are inefficient and redundant . a light-PEFT framework can be used to prune redundant parameters during training . |
| Approach: | They propose a parameter-efficient fine-tuning framework that freezes most parameters of the foundation model and finetuns only a small number of parameters. |
| Outcome: | The proposed framework achieves training and inference speedup, reduces memory usage, and maintains comparable performance and plug-and-play feature of PEFT. |
QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation (2024.emnlp-main)
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| Challenge: | Existing metrics fail to align well with human judgments when evaluating QG questions. |
| Approach: | They propose a multi-dimensional evaluation benchmark for QG and automatic metrics that evaluates questions and automated metrics across 7 dimensions. |
| Outcome: | The proposed benchmark evaluates QG models and automatic metrics across 7 dimensions . it shows that most QG model performs unsatisfactorily in terms of answerability and answer consistency . |
Pruning Large Language Models to Intra-module Low-rank Architecture with Transitional Activations (2024.findings-acl)
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| Challenge: | Structured pruning is a feasible solution for end-side LLM deployment . however, achieving a high compression ratio for scaled-up LLMs remains a challenge . |
| Approach: | They propose a task-agnostic structured pruning approach coupled with a compact Transformer architecture to prune LLMs into an intra-module low-rank architecture. |
| Outcome: | The proposed approach reduces transitional activations inside multi-head attention (MHA) and multi-layer perceptron (MLP) modules while preserving inter-module activations sensitive to perturbations. |
Synthesize, Prompt and Transfer: Zero-shot Conversational Question Generation with Pre-trained Language Model (2023.acl-long)
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| Challenge: | Existing research on QG focuses on generating single-turn questions, which are formalized as independent interactions. |
| Approach: | They propose a multi-stage knowledge transfer framework to leverage knowledge from single-turn question generation instances. |
| Outcome: | The proposed framework achieves 14.81 BLEU-4 (88.2% absolute improvement compared to T5) in CoQA with knowledge transferred from three single-turn datasets. |
Enhancing Zero-shot and Few-shot Stance Detection with Commonsense Knowledge Graph (2021.findings-acl)
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| Challenge: | Existing methods for stance detection are not applicable to zero-shot and few-shot scenarios. |
| Approach: | They propose a model that integrates commonsense knowledge into a stance detection model. |
| Outcome: | The proposed model outperforms state-of-the-art methods on zero-shot and few-shot stance detection tasks. |
Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow (2026.findings-acl)
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Yangyang Zhong, Yanmei Gu, Zhengqing Zang, Xiaomeng Li, Yuqi Ding, Xibei Jia, Yuting Shen, Zhenzhong Lan, Liwang Zhu, Weiping Liu, Junlin Zhou, Haisheng Liu, Zhong Xin Yu, Pengxin Luo, Donglian Qi, Yunfeng Yan, Junbo Zhao
| Challenge: | Autoregressive (AR) language models dominate modern natural language processing due to strong likelihood-based training objectives and reliable left-to-right decoding. |
| Approach: | They characterize MDLM behavior along two dimensions: parallelism strength and generation order . authors propose a Generate-then-Edit paradigm that mitigates dependency loss . |
| Outcome: | The proposed model improves on tasks that require "backward information" the Generate-then-Edit paradigm improves parallel decoding efficiency while reducing dependency loss. |
TailorKV: A Hybrid Framework for Long-Context Inference via Tailored KV Cache Optimization (2025.findings-acl)
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| Challenge: | Existing work mitigates memory overhead by offloading or compressing the Key-Value cache. |
| Approach: | They propose a method that integrates quantization and offloading into a generative large language model by using a hybrid compression method. |
| Outcome: | The proposed method outperforms the state-of-the-art in long-context evaluations. |
Maximum Entropy Loss, the Silver Bullet Targeting Backdoor Attacks in Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Existing backdoor defense paradigms focus on detecting and removing poisoned samples at pre-training or inference time. |
| Approach: | They propose a new approach where the backdoor attack is directly reversed by incorporating maximum entropy loss into training to neutralize the minimal cross-entropiness loss fine-tuning on poisoned data. |
| Outcome: | The proposed model significantly lowers the attack success rate on classification tasks and reduces the risk of backdoor attacks on clean data. |
Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference (2025.coling-main)
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Lanrui Wang, Jiangnan Li, Chenxu Yang, Zheng Lin, Hongyin Tang, Huan Liu, Yanan Cao, Jingang Wang, Weiping Wang
| Challenge: | Recent studies have focused on integrating commonsense knowledge into chatbots to enhance their ability to understand and generate dialogue responses. |
| Approach: | They propose a framework that integrates commonsense knowledge into chatbots to enable them to elicit more empathetic responses. |
| Outcome: | The proposed framework enables LLMs to uncover the implicit requirements of the conversation, aiming to elicit more empathetic responses. |
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning (2022.findings-emnlp)
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| Challenge: | Recent studies have shown that biased samples can be brittle for VQA models . however, the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data. |
| Approach: | They propose a contrastive learning approach that exploits biased samples for unbiased information that contributes to reasoning. |
| Outcome: | The proposed method achieves competitive performance on the OOD dataset while maintaining robustness on the ID dataset. |
Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation (2021.acl-long)
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| Challenge: | Knowledge distillation (KD) has shown great success in BERT compression. |
| Approach: | They propose a knowledge distillation paradigm that extracts the teacher's hidden state knowledge and then compresses it into three dimensions. |
| Outcome: | The proposed paradigm gives rise to training speedup of 2.7x 3.4x for two kinds of student models and computing devices. |
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training (2022.naacl-main)
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| Challenge: | Recent studies show pre-trained language models contain matching subnetworks that have similar transfer learning performance as the original PLM. |
| Approach: | They propose to prune matching subnetworks using magnitude-based pruning . they propose to optimize the subnetwork structure towards the pre-training objectives . |
| Outcome: | The proposed method is more efficient in searching subnetworks and advantageous when fine-tuning within a range of data scarcity. |
COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models (2022.emnlp-main)
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| Challenge: | Existing statically compressed pre-trained language models lack spatial and temporal efficiency due to their large size and wide width. |
| Approach: | They propose a spatially and temporally efficient model which retains the major capacity of PLMs. |
| Outcome: | The proposed model retains the major capacity of pre-trained language models at high compression and acceleration rate with 1/8 parameters and 1/19 FLOPs of BERT. |
Ranking and Sampling in Open-Domain Question Answering (D19-1)
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| Challenge: | Existing approaches focus on positive paragraphs which contain the answer during training, making it disturbed by similar but irrelevant paragraphs during testing. |
| Approach: | They propose a ranking model leveraging the paragraph-question and the paragraph relevance to compute a confidence score for each paragraph. |
| Outcome: | Experiments on three datasets show that the proposed model advances the state of the art. |
Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection (2022.coling-1)
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| Challenge: | stance detection is a task to identify attitudes from opinions towards certain targets, but it is expensive and time-consuming . stance detector is based on labeled data, but unlabeled data can be collected easier . |
| Approach: | They propose a semi-supervised framework for few-shot stance detection that uses unlabeled data to learn more distinguishable representations for different targets. |
| Outcome: | The proposed framework achieves state-of-the-art performance on multiple benchmark datasets. |
VecInfer: Efficient LLM Inference with Low-Bit KV Cache via Outlier-Suppressed Vector Quantization (2026.acl-long)
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| Challenge: | Existing quantization methods for large language models suffer performance degradation at ultra-low bit-widths due to key cache outliers. |
| Approach: | They propose a vector quantization method that suppresses outliers in the key cache and reduces memory access overhead. |
| Outcome: | The proposed method outperforms baseline quantization methods across long-context understanding and mathematical reasoning tasks while minimizing memory access overhead. |
A Gradient Control Method for Backdoor Attacks on Parameter-Efficient Tuning (2023.acl-long)
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| Challenge: | Parameter-Efficient Tuning (PET) fine-tunes pre-trained language models for downstream tasks, but a large reduction in the number of attackable parameters will greatly affect the effectiveness of backdoor attacks, resulting in backdoor forgetting. |
| Approach: | They propose a gradient control method to consolidate the attack effect by freezing most parameters of the pre-trained model and fine-tuning only a small number of parameters. |
| Outcome: | The proposed method improves sentiment classification and spam detection, and can be applied to different tasks. |
Adapt Once, Thrive with Updates: Transferable Parameter-Efficient Fine-Tuning on Evolving Base Models (2025.acl-long)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) is a common method for fine- tuning large language models . however, once updated, PEFT modules suffer performance degradation on newer versions . |
| Approach: | They propose a method that enhances the PEFT module by focusing on the task-specific pattern while reducing its dependence on certain knowledge in the base model. |
| Outcome: | Experiments show that PEFT modules can maintain performance on updated models without re-tuning . the proposed approach can be used in real-world applications with large model sizes . |
BeamLoRA: Beam-Constraint Low-Rank Adaptation (2025.acl-long)
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Naibin Gu, Zhenyu Zhang, Xiyu Liu, Peng Fu, Zheng Lin, Shuohuan Wang, Yu Sun, Hua Wu, Weiping Wang, Haifeng Wang
| Challenge: | Low-Rank Adaptation (LoRA) is one of the most efficient parameter-efficient fine-tuning methods. |
| Approach: | They propose to conceptualize each LoRA module as a beam where each rank corresponds to a potential sub-solution. |
| Outcome: | The proposed method improves performance on three base models and 12 datasets. |
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA (2022.findings-emnlp)
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Qingyi Si, Fandong Meng, Mingyu Zheng, Zheng Lin, Yuanxin Liu, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou
| Challenge: | Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution. |
| Approach: | They propose a dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. |
| Outcome: | The proposed dataset considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. |
Compressing and Debiasing Vision-Language Pre-Trained Models for Visual Question Answering (2023.emnlp-main)
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| Challenge: | Existing studies on VQA models have found that they suffer from dataset biases and inefficient memory footprints. |
| Approach: | They investigate whether a VLP can be compressed and debiased simultaneously by searching sparse and robust subnetworks. |
| Outcome: | The proposed compression and debiasing pipelines outperform the debiased full VLPs on VQA tasks. |
CBP-Tuning: Efficient Local Customization for Black-box Large Language Models (2025.emnlp-main)
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| Challenge: | Customized black-box prompt tuning is a new approach to customize large language models . however, as models grow, the resources required for training and deployment become increasingly expensive . |
| Approach: | They propose a framework that facilitates efficient local customization while preserving bidirectional privacy. |
| Outcome: | The proposed framework facilitates efficient local customization while preserving bidirectional privacy. |