Papers by Ziyue Li
Model Composition for Multimodal Large Language Models (2024.acl-long)
Copied to clipboard
Chi Chen, Yiyang Du, Zheng Fang, Ziyue Wang, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu
| Challenge: | Existing methods for creating versatile MLLMs rely on joint training with paired instruction data, which is resource-intensive and challenging to extend to new modalities. |
| Approach: | They propose a new paradigm for multimodal large language models by reusing modality encoders and merging LLM parameters. |
| Outcome: | The proposed model retains the modal understanding capabilities of each original model. |
Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage (2026.findings-acl)
Copied to clipboard
Junhao Hu, Fangze Li, Mingtao Xu, Feifan Meng, Shiju Zhao, Tiancheng Hu, Ting Peng, Anmin Liu, Wenrui Huang, Chenxu Liu, Ziyue Hua, Tao Xie
| Challenge: | Prior work typically decomposes inference into prefill and decode stages, with the decode stage dominating total latency. |
| Approach: | They propose an algorithm that detects threshold where information loss exceeds information gain during sparse decoding to reduce token consumption by up to 90% and a marginal accuracy degradation of less than 2%. |
| Outcome: | The proposed algorithm reduces token consumption by 90% with a marginal accuracy degradation of less than 2% across reasoning-intensive benchmarks. |
Enabling Stroke-Level Structural Analysis of Hieroglyphic Scripts without Language-Specific Priors (2026.findings-acl)
Copied to clipboard
Fuwen Luo, Zihao Wan, Ziyue Wang, Yaluo Liu, Pau Tong Lin Xu, Xuanjia Qiao, Xiaolong Wang, Peng Li, Yang Liu
| Challenge: | Existing structural analysis methods for hieroglyphic scripts are script-specific and labor-intensive. |
| Approach: | They propose a hieroglyphic Stroke Analyzer framework that captures character-internal structures and semantics without handcrafted data. |
| Outcome: | The proposed framework captures character-internal structures and semantics without priors . it can be used to generalize hieroglyphic scripts across languages . |
ActiView: Evaluating Active Perception Ability for Multimodal Large Language Models (2025.acl-long)
Copied to clipboard
Ziyue Wang, Chi Chen, Fuwen Luo, Yurui Dong, Yuanchi Zhang, Yuzhuang Xu, Xiaolong Wang, Peng Li, Yang Liu
| Challenge: | Existing benchmarks for evaluating MLLMs have not addressed active perception . a novel benchmark is proposed to evaluate active perception in ML models . |
| Approach: | They propose a benchmark to evaluate active perception in Multimodal Large Language Models . they restrict the perceptual field of a model and require it to actively zoom or shift it . |
| Outcome: | The proposed benchmark focuses on a specialized form of Visual Question Answering (VQA) that eases and quantifies the evaluation yet challenging for existing MLLMs. |
Sparser Mixture-of-Adapters with Cross-Layer Generalization (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for training large language models do not allow sharing adapters across layers . existing methods do not support sharing adapter pools, leading to redundancy and poor generalization . |
| Approach: | They propose a mixture-of-adapter framework that trains a pool of lightweight adapters at each layer and selects the most suitable ones for each input. |
| Outcome: | The proposed framework reduces active adapters by over 85% while boosting task accuracy. |
Perspective Transition of Large Language Models for Solving Subjective Tasks (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing . performance of LLMs on subjective tasks is limited, authors say . |
| Approach: | They propose a method that allows LLMs to select between direct, role, and third-person perspectives for best way to solve corresponding subjective problem. |
| Outcome: | The proposed method outperforms widely used single fixed perspective based methods on 12 subjective tasks. |
Browse and Concentrate: Comprehending Multimodal Content via Prior-LLM Context Fusion (2024.acl-long)
Copied to clipboard
Ziyue Wang, Chi Chen, Yiqi Zhu, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu
| Challenge: | Multimodal Large Language Models (MLLMs) lack understanding of multi-image and interleaved inputs due to the visual features encoded by frozen encoders before being fed into the LLM backbone. |
| Approach: | They propose a two phase paradigm to enable in-depth multimodal context fusion prior to feeding the features into LLMs. |
| Outcome: | The proposed paradigm boosts the performance on 7 multi-image scenarios, contributing to increments on average accuracy by 2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs, respectively. |
TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style Control (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing models fail to generate singing voices rich in stylistic nuances for unseen singers due to multifaceted nature of singing styles. |
| Approach: | They propose a zero-shot SVS model for style transfer across cross-lingual speech and singing styles and multi-level style control. |
| Outcome: | Experimental results show that TCSinger outperforms baseline models in synthesis quality, singer similarity, and style controllability. |
TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer Review (2025.emnlp-main)
Copied to clipboard
Yuan Chang, Ziyue Li, Hengyuan Zhang, Yuanbo Kong, Yanru Wu, Hayden Kwok-Hay So, Zhijiang Guo, Liya Zhu, Ngai Wong
| Challenge: | Large Language Models (LLMs) have shown significant potential in assisting peer review, but current methods struggle to generate thorough and insightful reviews while maintaining efficiency. |
| Approach: | They propose a framework that models paper review as a hierarchical and bidirectional question-answering process. |
| Outcome: | The proposed framework outperforms baselines on full review generation and actionable feedback comments generation tasks while reducing LLM token usage by up to 80% compared to computationally intensive approaches. |
Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing benchmarks focus on coarse-grained hallucination detection and fail to capture hallucinics . vision encoders exhibit unique hallucinian characteristics, but suboptimal of simple feature fusion. |
| Approach: | They propose a visual encoder that employs different training paradigms to instill inductive biases in visual encoded models. |
| Outcome: | The proposed system reduces hallucinations and improves model performance. |
Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)
Copied to clipboard
Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao
| Challenge: | Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications. |
| Approach: | They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions. |
| Outcome: | The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions. |
CAMIEval: Enhancing NLG Evaluation through Multidimensional Comparative Instruction-Following Analysis (2025.naacl-long)
Copied to clipboard
| Challenge: | Evaluating the quality of texts generated by language models has always been a challenging task in natural language processing (NLP). |
| Approach: | They propose a multidimensional comparative evaluation method based on instruction-following that combines relevance, factuality, and adherence with a concrete Chain-of-Thoughts process to enhance the accuracy of evaluations. |
| Outcome: | The proposed method outperforms existing methods in correlation with human evaluations on two NLG evaluation benchmarks. |
How Instruction and Reasoning Data shape Post-Training: Data Quality through the Lens of Layer-wise Gradients (2026.acl-long)
Copied to clipboard
| Challenge: | Spectral properties of low/high-quality instruction and reasoning data are used to explain finetuning dynamics in large language models. |
| Approach: | They propose to analyze layer-wise gradients induced by low/high-quality instruction and reasoning data for LLM post-training. |
| Outcome: | The results show that higher-quality data are associated with lower nuclear norms and higher effective ranks. |
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)
Copied to clipboard
Yilun Kong, Jingqing Ruan, YiHong Chen, Bin Zhang, Tianpeng Bao, Shi Shiwei, Du Qing, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, Xueqian Wang
| Challenge: | Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools. |
| Approach: | They propose a framework to enhance the task planning and tool usage abilities of LLMs in industrial systems. |
| Outcome: | The proposed framework enhances the task planning and tool usage abilities of LLM-based agents in industrial systems. |
MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria (2025.naacl-long)
Copied to clipboard
Wentao Ge, Shunian Chen, Hardy Chen, Nuo Chen, Junying Chen, Zhihong Chen, Wenya Xie, Shuo Yan, ChenghaoZhu ChenghaoZhu, Ziyue Lin, Dingjie Song, Xidong Wang, Anningzhe Gao, Zhang Zhiyi, Jianquan Li, Xiang Wan, Benyou Wang
| Challenge: | Existing evaluation methodologies for multimodal large language models are limited in evaluating objective queries without considering real-world user experiences. |
| Approach: | They propose to evaluate multimodal large language models with per-sample criteria using potent MLLM as the judge. |
| Outcome: | The proposed evaluation paradigm shows that it can be used to evaluate multimodal large language models with per-sample criteria. |
Rhythm Controllable and Efficient Zero-Shot Voice Conversion via Shortcut Flow Matching (2025.acl-long)
Copied to clipboard
Jialong Zuo, Shengpeng Ji, Minghui Fang, Mingze Li, Ziyue Jiang, Xize Cheng, Xiaoda Yang, Chen Feiyang, Xinyu Duan, Zhou Zhao
| Challenge: | Existing methods focus on disentangling speakers and content, while others focus on preserving the source's prosody. |
| Approach: | They propose a rhythm-controllable and efficient zero-shot voice conversion model that transforms the source speaker’s timbre into an unseen one while retaining speech content. |
| Outcome: | The proposed model adapts the linguistic content duration to the desired speaking style, facilitating the transfer of the target speaker’s rhythm. |
CODIS: Benchmarking Context-dependent Visual Comprehension for Multimodal Large Language Models (2024.acl-long)
Copied to clipboard
Fuwen Luo, Chi Chen, Zihao Wan, Zhaolu Kang, Qidong Yan, Yingjie Li, Xiaolong Wang, Siyu Wang, Ziyue Wang, Xiaoyue Mi, Peng Li, Ning Ma, Maosong Sun, Yang Liu
| Challenge: | Multimodal large language models have demonstrated promising results in a variety of tasks that combine vision and language. |
| Approach: | They propose a benchmark to assess the ability of models to use contextual information in free-form text to enhance visual comprehension. |
| Outcome: | The proposed model fails to extract and utilize contextual information to improve understanding of images. |
FiNE: Filtering and Improving Noisy Data Elaborately with Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Currently, there are two mainstream methods for improving data integrity: data filtering and data augmentation. |
| Approach: | They propose a method to improve data integrity by combining data filtering and data augmentation with LLMs. |
| Outcome: | The proposed method surpasses the open-source chat version on HalluQA by 8.45 on the open source version. |
MUCAR: Benchmarking Multilingual Cross-Modal Ambiguity Resolution for Multimodal Large Language Models (2025.emnlp-main)
Copied to clipboard
Xiaolong Wang, Zhaolu Kang, Wangyuxuan Zhai, Xinyue Lou, Yunghwei Lai, Ziyue Wang, Yawen Wang, Kaiyu Huang, Yile Wang, Peng Li, Yang Liu
| Challenge: | Existing multimodal benchmarks overlook linguistic and visual ambiguities, authors say . ambiguity resolution between modalities is lacking in multimodal large language models . |
| Approach: | They propose a benchmark to evaluate multimodal ambiguity resolution across multilingual and cross-modal scenarios. |
| Outcome: | a new benchmark evaluates multimodal ambiguity resolution across multilingual and cross-modal scenarios . the benchmark shows that MLLMs can resolve ambiguities in image-text alignment . however, existing benchmarks often overlook linguistic and visual ambiguties . |
MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding (2026.acl-long)
Copied to clipboard
Fuwen Luo, Shengfeng Lou, Chi Chen, Ziyue Wang, Chenliang Li, Weizhou Shen, Jiyue Guo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu
| Challenge: | Existing methods for MLLMs struggle with fine-grained temporal reasoning . despite advances in video understanding, current methods struggle with time-sensitive tasks . |
| Approach: | They propose a time-stamp-aware multi-segment grounding method that enhances temporal understanding by introducing timestamps. |
| Outcome: | The proposed method outperforms existing methods on time-sensitive tasks and generalizes well across diverse temporal understanding scenarios. |
Versatile Framework for Song Generation with Prompt-based Control (2025.findings-emnlp)
Copied to clipboard
Yu Zhang, Wenxiang Guo, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, Jingyu Lu, Rongjie Huang, Ruiyuan Zhang, Zhiqing Hong, Ziyue Jiang, Zhou Zhao
| Challenge: | Existing methods for song generation fail to generate vocals with prompt-based control and proper alignment. |
| Approach: | VersBand is a multi-task song generation framework for synthesizing high-quality songs with prompt-based control. |
| Outcome: | Experimental results show that VersBand performs better than baseline models across multiple song generation tasks. |
BrainLoc: Brain Signal-Based Object Detection with Multi-modal Alignment (2025.findings-emnlp)
Copied to clipboard
Jiaqi Duan, Xiaoda Yang, Kaixuan Luan, Hongshun Qiu, Weicai Yan, Xueyi Zhang, Youliang Zhang, Zhaoyang Li, Donglin Huang, JunYu Lu, Ziyue Jiang, Xifeng Yang
| Challenge: | BrainLoc is a lightweight object detection model guided by fMRI signals. |
| Approach: | They propose a brain-based object detection model guided by fMRI signals . they employ a multi-modal alignment strategy that enhances fmr feature extraction . |
| Outcome: | The proposed model improves fMRI-based object detection accuracy and convenience. |
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) demonstrate impressive reasoning ability and the maintenance of world knowledge in natural language tasks. |
| Approach: | They propose a framework that enables LLMs to ask relevant questions to uncover more details in the image, along with filters for refining the generated information. |
| Outcome: | The proposed framework boosts the performance of baseline methods by 2.15% on OK-VQA and achieves consistent improvements across different LLMs. |
GuiLoMo: Allocating Experts and Ranks for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors (2025.findings-emnlp)
Copied to clipboard
Xinrong Chen, Hengyuan Zhang, Yingmin Qiu, Xiao Liang, Ziyue Li, Guanyu Wang, Weiping Li, Tong Mo, Hayden Kwok-Hay So, Ngai Wong
| Challenge: | Low-Rank Adaptation (LoRA) methods are efficient for a large language model with reduced computational costs. |
| Approach: | They propose a layer-wise expert numbers and ranks allocation strategy with GuidedSelection Vectors. |
| Outcome: | The proposed method achieves superior or comparable performance to all baselines on three backbone models. |
DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms (2025.findings-emnlp)
Copied to clipboard
Xiaojun Bi, Shuo Li, Junyao Xing, Ziyue Wang, Fuwen Luo, Weizheng Qiao, Lu Han, Ziwei Sun, Peng Li, Yang Liu
| Challenge: | Dongba pictographic is the only pictograph script still in use in the world. |
| Approach: | DongbaMIE is the first dataset focusing on multimodal information extraction of Dongbe pictographs. |
| Outcome: | The dataset contains 23,530 sentence-level and 2,539 paragraph-level high-quality text-image pairs. |