Papers by Qintong Li
Stop Looking for “Important Tokens” in Multimodal Language Models: Duplication Matters More (2025.emnlp-main)
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Zichen Wen, Yifeng Gao, Shaobo Wang, Junyuan Zhang, Qintong Zhang, Weijia Li, Conghui He, Linfeng Zhang
| Challenge: | Vision tokens in multimodal large language models often dominate computational overhead due to excessive length compared to linguistic modality. |
| Approach: | They propose a token pruning method which defines an importance criterion for vision tokens and prunes the unimportant vision token during inference. |
| Outcome: | The proposed method can prune 88.9% of vision tokens while maintaining comparable performance. |
Event Transition Planning for Open-ended Text Generation (2022.findings-acl)
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| Challenge: | Open-ended text generation tasks require models to generate coherent continuation given limited preceding context. |
| Approach: | They propose a novel two-stage method which explicitly arranges ensuing events in open-ended text generation tasks. |
| Outcome: | The proposed method improves coherence and diversity of open-ended text generation tasks. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models (2024.findings-acl)
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Xueliang Zhao, Xinting Huang, Tingchen Fu, Qintong Li, Shansan Gong, Lemao Liu, Wei Bi, Lingpeng Kong
| Challenge: | Multimodal reasoning is a key capability for large vision-language models . however, the vanilla Chain-of-Thought method fails to address critical steps in multi-step reasoning tasks. |
| Approach: | They propose a bi-modal Behavioral Alignment method to augment multimodal reasoning . they use domain-specific language to integrate multimodal information into a precise alternative form . |
| Outcome: | The proposed method significantly improves GPT-4V(ision) on geometry problem solving, chess positional advantage prediction and molecular property prediction. |
A Cognitive Stimulation Dialogue System with Multi-source Knowledge Fusion for Elders with Cognitive Impairment (2023.acl-long)
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| Challenge: | Existing cognitive stimulation systems lack data on how to integrate emotional support and therapy principles into chit-chat dialogue systems. |
| Approach: | They propose a multi-source knowledge fusion method for CS dialogue to generate open-ended responses guided by the therapy principle and emotional support strategy. |
| Outcome: | The proposed method generates open-ended responses guided by the therapy principle and emotional support strategy of the target response. |
EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation (2020.coling-main)
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| Challenge: | Existing work on empathetic dialogue generation fails to capture the nuances of human emotion and consider the potential of user feedback. |
| Approach: | They propose a multi-resolution adversarial model - EmpDG - to generate more empathetic responses by exploiting both coarse-grained dialogue-level and fine-grounded token-level emotions. |
| Outcome: | The proposed model outperforms the state-of-the-art models in both content quality and emotion perceptivity. |
Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks (2025.coling-main)
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| Challenge: | Existing work uses large language models (LLMs) to evaluate natural language process tasks, but there are shortcomings in current LLMs. |
| Approach: | They examine the alignment between LLM evaluators and human annotators by comparing conventional and alignment tasks with different evaluation criteria. |
| Outcome: | The proposed models excel in general criteria, such as fluency, but face challenges with complex criteria, including numerical reasoning. |
GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers (2024.acl-long)
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| Challenge: | Large language models (LLMs) have demonstrated impressive performance across various mathematical reasoning benchmarks. |
| Approach: | They introduce an adversarial grade school math dataset and explore whether LLMs can be more robust when questions are slightly changed. |
| Outcome: | The proposed method generates and verifies each intermediate thought based on its reasoning goal and calculation result. |
Explanation Regeneration via Information Bottleneck (2023.findings-acl)
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| Challenge: | Recent work builds on prompt engineering to generate free-text explanations without specific training, but they lack sufficiency and conciseness due to the prompt complexity and hallucination issues. |
| Approach: | They propose to generate explanations via the information bottleneck theory by polishing the single-pass output of large pretrained language models but retaining the information that supports the contents being explained by balancing two information bottle neck objectives. |
| Outcome: | The proposed explanations are based on the information bottleneck theory . they are able to explain black-box predictions naturally and accurately . |
Retrieved Sequence Augmentation for Protein Representation Learning (2024.emnlp-main)
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Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, Sheng Wang, Lingpeng Kong
| Challenge: | Using multiple sequence alignments (MSA) to extract evolutionary knowledge is limited. |
| Approach: | They propose to use multiple sequence alignments to augment protein representations . they propose to employ Retrieved Sequence Augmentation to enhance protein representation learning . |
| Outcome: | The proposed method surpasses MSA Transformer by 5% in structural and property prediction tasks while being 373 times faster. |
ZeroGen: Efficient Zero-shot Learning via Dataset Generation (2022.emnlp-main)
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| Challenge: | Existing approaches to generate training data with pre-trained language models have been found effective in various scenarios. |
| Approach: | They propose an unsupervised zero-shot learning method that generates a dataset from scratch and trains a tiny task model under supervision of the synthesized dataset. |
| Outcome: | The proposed method is annotated-free and efficient, but can provide useful insights from the perspective of data-free model-agnostic knowledge distillation and unreferenced text generation evaluation. |
MAGE: Machine-generated Text Detection in the Wild (2024.acl-long)
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Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, Yue Zhang
| Challenge: | Existing research has focused on evaluating detection methods for specific domains or language models. |
| Approach: | They build a testbed to detect texts from diverse human writings and LLMs using different detection methods. |
| Outcome: | Empirical results show that the top performing detector can identify 84.12% out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios. |