Papers by Yaowei Wang
Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors (2025.emnlp-main)
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Hao Fang, Jiawei Kong, Tianqu Zhuang, Yixiang Qiu, Kuofeng Gao, Bin Chen, Shu-Tao Xia, Yaowei Wang, Min Zhang
| Challenge: | Existing methods to detect large language models (LLMs) generated for plagiarism use paraphrases to rewrite them to evade detection. |
| Approach: | They propose a training-free method that effectively fools text detectors using off-the-shelf LLMs by rewriting them to evade detection. |
| Outcome: | The proposed method deceives text detectors using off-the-shelf LLMs by rewriting them to produce human-like sentences that are less discernible by detectors. |
HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models (2026.findings-acl)
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| Challenge: | Existing methods for visual token pruning lack insight into the intrinsic property of the vision encoder . et al., 2017: 99.3% of task accuracy with only 1/3 of the tokens. |
| Approach: | They propose a model-agnostic token pruning method that trains without training . they propose 'HiPrune' method which prunes visual tokens according to their attention . |
| Outcome: | The proposed method achieves 99.3% of task accuracy with only 1/3 of the tokens . it reduces inference FLOPs by 58.7% and maintains 99.99% accuracy with 2/9 tokens. |
PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models (2025.acl-long)
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| Challenge: | Existing methods for sub 2-bit quantization introduce an extra 1-bit or more per weight. |
| Approach: | They propose a sub 2-bit post-training quantization method that enables weight quantization to 1.61-bit for the first time. |
| Outcome: | The proposed method reduces the upper bound of quantization error to 1.61-bit for the first time. |
Efficient Safety Alignment of Large Language Models via Preference Re-ranking and Representation-based Reward Modeling (2025.acl-long)
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| Challenge: | Existing safety alignment methods for Large Language Models (LLMs) face the distribution shift issue, which requires significant computational resources. |
| Approach: | They propose a framework that leverages the model’s intrinsic safety judgment capability to extract reward signals, which are then used to calculate label confidence for preference reordering. |
| Outcome: | The proposed framework improves safety performance while avoiding 300x computational overheads. |
MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning (2023.acl-long)
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| Challenge: | Existing methods for supervised visual captioning require large scale of images or videos paired with descriptions in a specific language. |
| Approach: | They propose a zero-shot approach that generates captions for different scenarios without labeling . they use concept prompts to retrieve concepts and auto-encode them to learn writing styles . |
| Outcome: | The proposed approach generates captions for different scenarios and languages without labeled vision-caption pairs. |
Digging out Discrimination Information from Generated Samples for Robust Visual Question Answering (2023.findings-acl)
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| Challenge: | Existing methods to solve this problem rely on additional annotations and generate negative samples . |
| Approach: | They propose a method to Dig out Discrimination information from Generated samples to address these limitations. |
| Outcome: | The proposed method improves on the visual question-answering datasets. |
Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents (2025.emnlp-demos)
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| Challenge: | Existing data synthesis tools struggle to extract reliable fine-tuning data from heterogeneous documents. |
| Approach: | They propose a framework for synthesizing fine-tuning data from unstructured documents via an intuitive graphical user interface. |
| Outcome: | The proposed framework can extract reliable data from unstructured documents via an intuitive graphical user interface (GUI) it leverages persona-driven prompting approach to generate diverse question-answer pairs using public-available LLMs. |
VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension (2025.acl-long)
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| Challenge: | Existing video evaluation benchmarks focus on a single language, typically English, and feature videos rooted in Western cultural contexts. |
| Approach: | They propose a video evaluation benchmark designed to bridge cultural, linguistic, and domain divide in video comprehension. |
| Outcome: | The proposed video evaluation benchmark bridges cultural, linguistic, and domain divides . existing benchmarks only feature videos from YouTube, Shutterstock, or established video datasets based on cultural diversity . |
From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents (2026.acl-long)
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| Challenge: | Existing multimodal large language models struggle with long-horizon video understanding due to limited context windows and static memory mechanisms that fail to mirror human cognitive efficiency. |
| Approach: | They propose a pyramidal multimodal memory architecture grounded in Fuzzy-Trace Theory that structures memory hierarchically into a *Sensory Buffer*, *Episodic Stream*, and *Symbolic Schema*. |
| Outcome: | The proposed architecture achieves state-of-the-art on both offline and streaming tasks, demonstrating robust generalization and validating the effectiveness of cognition-inspired memory organization. |
Modeling Uncertainty in Composed Image Retrieval via Probabilistic Embeddings (2025.acl-long)
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Haomiao Tang, Jinpeng Wang, Yuang Peng, GuangHao Meng, Ruisheng Luo, Bin Chen, Long Chen, Yaowei Wang, Shu-Tao Xia
| Challenge: | Composed Image Retrieval (CIR) combines text and reference images to search for images . metric learning methods that focus on point embeddings fail to capture uncertainty in input data . |
| Approach: | They propose a framework that captures uncertainty in images and queries by Gaussian distributions in latent space rather than fixed points. |
| Outcome: | Experiments show that the proposed framework quantifies quality and semantic uncertainties . it can handle polysemy and ambiguity in search intentions, authors say . |
A Unified Agentic Framework for Evaluating Conditional Image Generation (2025.acl-long)
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Jifang Wang, Yangxue Yangxue, Longyue Wang, Zhenran Xu, Yiyu Wang, Yaowei Wang, Weihua Luo, Kaifu Zhang, Baotian Hu, Min Zhang
| Challenge: | Conditional image generation is a popular and personalization-oriented task, but there are challenges in developing task-agnostic, reliable, and explainable evaluation metrics. |
| Approach: | They propose a unified agentic framework for comprehensive evaluation of conditional image generation tasks. |
| Outcome: | The proposed framework achieves a high correlation with human assessments on seven prominent image generation tasks. |
Latent-Condensed Transformer for Efficient Long Context Modeling (2026.acl-long)
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| Challenge: | Existing approaches address these bottlenecks separately: Multi-head Latent Attention (MLA) reduces the KV cache by projecting tokens into a low-dimensional latent space, while sparse attention reduces computation. |
| Approach: | They propose a Latent-Condensed Attention mechanism that performs structured context condensation directly within MLA's latent space. |
| Outcome: | The proposed approach reduces KV cache size and attention cost without adding parameters. |