Papers by Hui Wei
CodeArena: Evaluating and Aligning CodeLLMs on Human Preference (2025.emnlp-main)
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Jian Yang, Jiaxi Yang, Wei Zhang, Jin Ke, Yibo Miao, Lei Zhang, Liqun Yang, Zeyu Cui, Yichang Zhang, Zhoujun Li, Binyuan Hui, Junyang Lin
| Challenge: | Code large language models (codeLLMs) focus on synthesizing the correct code snippet, ignoring the alignment with human preferences. |
| Approach: | They propose a benchmark code-based on 40 categories and 44 programming languages to emulate real-world coding tasks. |
| Outcome: | The proposed benchmarks show that open-source code LLMs perform better than open-sourced ones. |
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web (2025.findings-acl)
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Hongcheng Guo, Wei Zhang, Junhao Chen, Yaonan Gu, Jian Yang, Junjia Du, Shaosheng Cao, Binyuan Hui, Tianyu Liu, Jianxin Ma, Chang Zhou, Zhoujun Li
| Challenge: | Existing models have been introduced to improve image comprehension, but there is no robust benchmark for imagetoweb conversion. |
| Approach: | They propose a benchmark to assess imagetoweb conversion proficiency of large multimodal models . they propose to measure layout information of web pages by parsing the Document Object Model tree . |
| Outcome: | The proposed benchmark measures the layout information of web pages—i.e., the positional relationships between elements—which has been overlooked by prior work. |
Turning the Tide: Repository-based Code Reflection (2025.findings-emnlp)
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| Challenge: | Code large language models (LLMs) enhance programming by understanding and generating code across languages. |
| Approach: | a new benchmark evaluates code understanding and generation in repositories using code large language models. |
| Outcome: | The proposed model improves code understanding and generation in repositories by evaluating 1,888 test cases across 6 programming languages. |
Temporal Scaling Law for Large Language Models (2025.emnlp-main)
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Yizhe Xiong, Xiansheng Chen, Xin Ye, Hui Chen, Zijia Lin, Haoran Lian, Zhenpeng Su, Wei Huang, Jianwei Niu, Jungong Han, Guiguang Ding
| Challenge: | Existing studies have found that the test loss of LLMs scales as power-laws with model size, computational budget, and dataset size. |
| Approach: | They propose a concept of Temporal Scaling Law to study test loss of LLMs . they break down test loss into fine-grained token positions and develop a dynamic hyperbolic-law . |
| Outcome: | The proposed model predicts the test loss of LLMs as the training steps scale up. |
Do VLMs Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models (2026.findings-acl)
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Zhining Liu, Tianyi Wang, Xiao Lin, Penghao Ouyang, Gaotang Li, Ze Yang, Hui Liu, Sumit Keswani, Vishwa Pardeshi, Huijun Zhao, Wei Fan, Hanghang Tong
| Challenge: | Vision-Language Models (VLMs) have advanced multimodal learning, driving progress in cross-modal reasoning. |
| Approach: | They propose to examine moral robustness of vision-language models by analyzing their moral stances under multimodal perturbations. |
| Outcome: | The proposed model-agnostic multimodal perturbations expose VLMs to a variety of moral vulnerabilities, including a sycophancy trade-off where stronger instruction-following models are more susceptible to persuasion. |
Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training (2026.acl-long)
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Yifan Yang, Bing Han, Hui Wang, Wei Wang, Ziyang Ma, Long Zhou, Zengrui Jin, Guanrou Yang, Tianrui Wang, Xu Tan, Xie Chen
| Challenge: | Existing models for fine-grained speaking styles are limited in terms of accuracy, coverage, and naturalness. |
| Approach: | They propose a model that pre-trains with coarse captions and annotates with a pipeline that grounds captions in audio. |
| Outcome: | The proposed model outperforms existing models with fine-grained style annotations . it integrates global and fine-granular supervision, enabling unified representations based on the proposed model . |
VisiPruner: Decoding Discontinuous Cross-Modal Dynamics for Efficient Multimodal LLMs (2025.emnlp-main)
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| Challenge: | Multimodal Large Language Models (MLLMs) suffer from significant computational overhead due to the quadratic growth of attention computations with the number of multimodal tokens. |
| Approach: | They propose a training-free pruning framework that prunes multimodal tokens without a trained pruning method. |
| Outcome: | The proposed pruning framework outperforms existing token pruning methods and generalizes across diverse MLLMs. |
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models (2024.lrec-main)
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| Challenge: | Existing methods for incorporating external knowledge into language models do not prioritize learning embeddings for entity-related tokens. |
| Approach: | They propose a framework for incorporating external knowledge into pre-training models that utilize entity-related tokens. |
| Outcome: | The proposed framework reduces pre-training time by 50% and outperforms other KEPLMs in knowledge probing tasks and multiple knowledge-aware language understanding tasks. |
PlanGenLLMs: A Modern Survey of LLM Planning Capabilities (2025.acl-long)
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| Challenge: | Existing studies have focused on developing LLMs to automate complex planning tasks. |
| Approach: | They propose to provide a comprehensive overview of current LLM planners to fill this gap . they examine performance criteria including completeness, executability, optimality, representation, generalization, and efficiency . |
| Outcome: | The proposed survey examines performance criteria for LLM planners and highlights their strengths and weaknesses. |
MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences (2022.emnlp-main)
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| Challenge: | Existing approaches to multimodal learning assume a complete input modality setting, i.e., each modality is either complete or completely missing in both training and test sets. |
| Approach: | They propose an alignment dynamics learning module based on the theory of optimal transport for missing data imputation and a denoising training algorithm to enhance the quality of iputation and accuracy of model predictions. |
| Outcome: | The proposed method performs faster and more accurate inferences under different missing conditions and alleviates the overfitting issue. |
Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models (2026.acl-long)
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Wei Wu, Liyi Chen, Congxi Xiao, Tianfu Wang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Hui Xiong
| Challenge: | Existing efficient reasoning methods rely on explicit length penalties for excessive verbosity on simple queries. |
| Approach: | They propose a training-time intervention that selectively suppresses redundant tokens . they find length shift occurs when models generate unnecessary reasoning on trivial inputs - a phenomenon that is often unexplored . |
| Outcome: | The proposed method reduces inference token usage by 78% while increasing accuracy compared to the initial policy and surpasses state-of-the-art efficient reasoning methods. |
Facilitating Long Context Understanding via Supervised Chain-of-Thought Reasoning (2025.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have enabled them to process increasingly longer sequences, ranging from 2K to 2M tokens and even beyond. |
| Approach: | They propose a synthetic dataset in the financial domain that integrates Chain-of-Thought reasoning into LLMs in a supervised manner to facilitate effective long-context understanding. |
| Outcome: | The proposed model outperforms standard GPT-4o-mini on the Loong benchmark and fine tunes LLaMA-3.1-8B-Instruct on the model, achieving a 28.0% gain on the financial subset. |
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. |
DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification (2022.coling-1)
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| Challenge: | Existing methods to improve the robustness of text classification models are token-, sentence-, and hiddenlevel augmentation. |
| Approach: | They propose an interpolation-based data augmentation approach called DoubleMix to improve the robustness of text classification models by learning the “shifted” features in hidden space. |
| Outcome: | The proposed approach outperforms several popular methods on six text classification benchmark datasets and visual analysis shows that the model features are highly interpretable. |
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers (2022.coling-1)
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Bowen Qin, Lihan Wang, Binyuan Hui, Bowen Li, Xiangpeng Wei, Binhua Li, Fei Huang, Luo Si, Min Yang, Yongbin Li
| Challenge: | Existing methods that learn from multiple semantically-equivalent questions are limited to one-to-one mapping . |
| Approach: | They propose a constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions and learn robust feature representations with reduced spurious associations. |
| Outcome: | The proposed method outperforms strong competitors and achieves state-of-the-art results on five benchmark datasets. |
Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition (2026.findings-eacl)
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| Challenge: | Existing systems for conversational recommender systems (CRS) have strong results in movies, but games present distinct challenges . MATCHA framework provides specialized agents for intent parsing, tool-augmented retrieval, multi-LLM ranking, and stronger safety. |
| Approach: | They propose a framework for conversational recommender systems that assigns specialized agents for intent parsing, tool-augmented retrieval, multi-LLM ranking and risk control. |
| Outcome: | MATCHA outperforms baselines on real user request dataset, improves Hit@5 by 20%, reduces popularity bias by 24%, and achieves 97.9% adversarial defense. |
PREMISE: Matching-based Prediction for Accurate Review Recommendation (2025.findings-naacl)
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| Challenge: | Experimental results show that PREMISE achieves promising performance with less computational cost. |
| Approach: | They propose a new architecture for matching-based learning in multimodal fields for the MRHP task. |
| Outcome: | The proposed architecture significantly boosts performance on multimodal tasks with less computational cost compared to the state-of-the-art fusion-based methods. |
SVD-GCL: A Noise-Augmented Hybrid Graph Contrastive Learning Framework for Recommendation (2025.coling-main)
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| Challenge: | Recent advances in graph neural networks have made it difficult to capture user preferences. |
| Approach: | They propose a graph contrastive learning recommendation model based on noise augmentation that integrates truncated singular value decomposition in the feature engineering stage. |
| Outcome: | The proposed model reduces dimensionality and denoises the original data. |
AIR: Complex Instruction Generation via Automatic Iterative Refinement (2025.emnlp-main)
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| Challenge: | Existing methods for generating complex instructions are resource-intensive and lack diversity. |
| Approach: | They propose a framework to generate complex instructions with constraints using a document-generated initial instruction and an iterative refinement framework to incorporate LLM-as-judge guidance. |
| Outcome: | The proposed framework significantly outperforms existing methods for generating complex instructions, and outperformed existing methods. |
TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection (2025.emnlp-main)
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| Challenge: | Rapid advances in Large Language Models have spurred demand for processing extended context sequences . however, performance degradation due to sequence lengths out-of-distribution and excessively long inference times are limiting LLMs in long-context scenarios. |
| Approach: | They propose a training-free method for efficient and accurate long-context inference . they selectively involves a few critical KV cache tokens in attention calculation . |
| Outcome: | The proposed method speeds up attention computation and accelerates inference time while reducing selection overhead. |
Fast Quiet-STaR: Thinking Without Thought Tokens (2025.findings-emnlp)
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| Challenge: | Large Language Models have achieved impressive performance across a range of tasks, but further gains require more than scaling up model sizes or training data. |
| Approach: | They propose a method that gradually reduces the number of thought tokens . this method allows models to internalize more abstract reasoning processes . |
| Outcome: | The proposed framework preserves the benefits of token-level reasoning while reducing computational cost. |
Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis (2021.emnlp-main)
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| Challenge: | Existing work on multimodal sentiment analysis relies on back-propagated task loss or geometric property of feature spaces to produce favorable fusion results. |
| Approach: | They propose a framework which hierarchically maximizes the Mutual Information (MI) in unimodal input pairs and between multimodal fusion result and unimod input to maintain task-related information through multimodal integration. |
| Outcome: | The proposed framework maximizes the Mutual Information (MI) in unimodal input pairs and between multimodal fusion result and unimodulated input to maintain task-related information through multimodal integration. |
SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training (2022.findings-emnlp)
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| Challenge: | Existing methods for semi-supervised text classification have shown great performance in few-shot scenarios, where both labeled and unlabeled data are utilized. |
| Approach: | They propose a simple instance-adaptive self-training method for semi-supervised text classification that generates two augmented views for each unlabeled data and trains a meta learner to identify relative strength of augmentations based on the similarity between the original view and the augmented view. |
| Outcome: | The proposed method consistently shows competitive performance with varying sizes of labeled training data compared to existing semi-supervised learning methods. |
UER: An Open-Source Toolkit for Pre-training Models (D19-3)
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Zhe Zhao, Hui Chen, Jinbin Zhang, Xin Zhao, Tao Liu, Wei Lu, Xi Chen, Haotang Deng, Qi Ju, Xiaoyong Du
| Challenge: | Existing work on pre-training models have shown that it is important to use a framework to deploy various pre- training models efficiently. |
| Approach: | They propose an assemble-on-demand pre-training toolkit that assembles pre-trained models on demand and encapsulates them with rich modules. |
| Outcome: | The proposed framework can reproduce state-of-the-art models or develop models that remain unexplored. |
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning (2022.coling-1)
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| Challenge: | e-commerce has become a research hotspot for review helpfulness prediction . a new approach to help predict helpfulness of multimodal product reviews is proposed . |
| Approach: | They propose a machine learning task to identify helpfulness of multimodal product reviews . they use a probe-based strategy to enforce high attention weights on regions of greater significance . |
| Outcome: | The proposed model achieves state-of-the-art performance with lower memory consumption on two benchmark datasets with three categories. |
Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models (2025.coling-main)
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Haoran Lian, Junmin Chen, Wei Huang, Yizhe Xiong, Wenping Hu, Guiguang Ding, Hui Chen, Jianwei Niu, Zijia Lin, Fuzheng Zhang, Di Zhang
| Challenge: | Recent studies show that Large language models struggle with handling long token sequences due to limited training context size. |
| Approach: | They propose a single-stage continual pretraining method to equip LLMs with long context modeling capabilities. |
| Outcome: | The proposed method outperforms existing methods on 4 language modeling benchmarks. |
Qwen2.5-xCoder: Multi-Agent Collaboration for Multilingual Code Instruction Tuning (2025.acl-long)
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Jian Yang, Wei Zhang, Yibo Miao, Shanghaoran Quan, Zhenhe Wu, Qiyao Peng, Liqun Yang, Tianyu Liu, Zeyu Cui, Binyuan Hui, Junyang Lin
| Challenge: | Existing methods to train code LLMs view each programming language in isolation . experimental results show that Qwen2.5-xCoder can bridge the gap between different programming languages . |
| Approach: | They propose a framework that allows agents to collaborate to enhance multilingual instruction tuning for code LLMs. |
| Outcome: | Experimental results show that Qwen2.5-xCoder can transfer knowledge efficiently and effectively between languages. |
DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs (2025.emnlp-main)
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Minxuan Lv, Zhenpeng Su, Leiyu Pan, Yizhe Xiong, Zijia Lin, Hui Chen, Wei Zhou, Jungong Han, Guiguang Ding, Wenwu Ou, Di Zhang, Kun Gai, Songlin Hu
| Challenge: | Existing sparsification methods like pruning can lose model knowledge through parameter removal. |
| Approach: | They propose a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. |
| Outcome: | The proposed approach achieves superior performance across language modeling and downstream tasks under equivalent computational constraints. |
Learning to Instruct: Fine-Tuning a Task-Aware Instruction Optimizer for Black-Box LLMs (2025.findings-emnlp)
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Yunzhe Qi, Jinjin Tian, Tianci Liu, Ruirui Li, Tianxin Wei, Hui Liu, Xianfeng Tang, Monica Xiao Cheng, Jingrui He
| Challenge: | Learning to Instruct is a new paradigm for black-box LLMs with inaccessible internal states. |
| Approach: | They propose a new paradigm that formulates instruction optimization as an LLM fine-tuning objective for a white-box “instruction engineer” LLM. |
| Outcome: | The proposed framework outperforms strong baselines in performance and efficiency. |
Monte Carlo Tree Search Based Prompt Autogeneration for Jailbreak Attacks against LLMs (2025.coling-main)
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| Challenge: | Jailbreak attacks craft specific prompts or append adversarial suffixes to prompts, thereby inducing language models to generate harmful or unethical content and bypassing the model’s safety guardrails. |
| Approach: | They propose a Monte Carlo Tree Search (MCTS) based Prompt Auto-generation (MPA) method to generate adversarial suffixes for valid jailbreak attacks. |
| Outcome: | The proposed method outperforms existing methods on open-source and closed-source models and shows that it can generate harmful responses. |
Enhancing Multimodal Named Entity Recognition through Adaptive Mixup Image Augmentation (2025.coling-main)
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| Challenge: | Current named entity recognition methods struggle with text-image mismatch problem due to a lack of visual context. |
| Approach: | They propose an adaptive mixup image augmentation method that generates augmented images based on matching score between text and image . |
| Outcome: | The proposed method can be integrated into existing models and demonstrate consistent performance improvements. |
MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language Models (2024.findings-naacl)
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| Challenge: | Existing generative language models neglect an inherent challenge in text corpus during training, i.e., the imbalance between frequent tokens and infrequent ones. |
| Approach: | They propose a function to mitigate the imbalance between frequent and infrequent tokens . authors propose 'MiLe Loss' function to assess learning difficulty of tokens during training . |
| Outcome: | Experiments show that models with proposed model can improve on downstream benchmarks. |
AgentMark: Utility-Preserving Behavioral Watermarking for Agents (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have improved text generation and reasoning. |
| Approach: | They propose a behavioral watermarking framework that embeds multi-bit identifiers into planning decisions while preserving utility. |
| Outcome: | The proposed framework embeds multi-bit provenance into planning decisions while preserving utility. |
Ranking-Based Autoencoder for Extreme Multi-label Classification (N19-1)
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| Challenge: | Existing methods to solve label dependency and noisy labeling problems are limited . experimental results show the proposed method is competitive to state-of-the-art methods . |
| Approach: | They propose a deep learning XML method with word-vector-based self-attention followed by ranking-based AutoEncoder architecture to solve these problems. |
| Outcome: | The proposed method is competitive to state-of-the-art methods on benchmark datasets. |
Self-Adaptive Sampling for Accurate Video Question Answering on Image Text Models (2024.findings-naacl)
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| Challenge: | Image–text models (ITMs) are the prevalent architecture to solve video question–answering tasks, which requires only a few input frames to save huge computational cost compared to video–language models. |
| Approach: | They propose a sampling method based on question–frame correlation that is efficient for the few-frame situations. |
| Outcome: | The proposed method can boost the performance of image–text pretrained models and have a wide application scenario in terms of model architectures and dataset types. |