Papers by Qian Sun
Recyclable Tuning for Continual Pre-training (2023.findings-acl)
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Yujia Qin, Cheng Qian, Xu Han, Yankai Lin, Huadong Wang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Continual pre-training is the paradigm where pre-trained language models acquire fresh knowledge and gradually get upgraded. |
| Approach: | They propose to use adapted weights to recycle old PLMs for continual pre-training . they propose to combine initialization and distillation methods to achieve better performance . |
| Outcome: | The proposed method improves the convergence and performance of the upgraded PLM. |
RSMeM: Knowledge-Enhanced Memory Evolution for Remote Sensing Agents with Systematic Evaluation (2026.acl-long)
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Bingxian Wu, Yu Zhang, Zonghao Guo, Tang Liu, Chen Qian, Yuxiang Lu, Xingbo Du, Yanghao Li, Yidan Zhang, Chi Chen, Ling Yao, Chenghu Zhou, Maosong Sun
| Challenge: | Existing RS agents built on general-purpose LLMs are domain-agnostic, resulting in brittle and error-prone workflows. |
| Approach: | They propose a knowledge-enhanced memory evolution mechanism that bootstraps RS agents with pre-distilled domain knowledge and iteratively integrates online experience for robust multi-step tool execution. |
| Outcome: | Experiments show that the new model improves tool-use performance and accuracy . iteratively, iteration of the model integrates online experience for robust multi-step tool execution . |
Empowering Math Problem Generation and Reasoning for Large Language Model via Synthetic Data based Continual Learning Framework (2025.emnlp-main)
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| Challenge: | Existing learning frameworks for large language models (LLMs) for math problem generation are limited and lack quality data. |
| Approach: | They propose a synthetic data based continual learning framework to improve LLMs ability for MPG and math reasoning. |
| Outcome: | The proposed framework improves performance on large language models and math reasoning using supervised fine-tuning, data synthesis and direct preference optimization. |
BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain (2024.findings-emnlp)
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| Challenge: | Existing work adopts separate modules for retrieval and generation, which may be suboptimal since the retrieval task and generation task cannot benefit from each other to improve performance. |
| Approach: | They propose a backbone-shared RAG framework that uses a domain-specific corpus to continuously pre-train a model and then trains two plug-and-play Low-Rank Adaptation modules based on the shared backbone to minimize retrieval and generation losses respectively. |
| Outcome: | The proposed framework outperforms baseline models by 5% and 13% in Hit@3 upon two datasets in retrieval evaluation and by 23% in terms of BLEU-3 in generation evaluation. |
Few-Shot Multimodal Named Entity Recognition Based on Mutlimodal Causal Intervention Graph (2024.lrec-main)
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| Challenge: | Existing methods for multimodal named entity recognition are limited due to limited resources. |
| Approach: | They propose a Few-shot Multimodal Named Entity Recognition task to address these relation types by constructing a multimodal graph and a new multimodal causal intervention strategy. |
| Outcome: | The proposed model improves on two multimodal named entity recognition datasets. |
Exploring Mode Connectivity for Pre-trained Language Models (2022.emnlp-main)
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| Challenge: | Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, starting from which high-performance minima could be found. |
| Approach: | They investigate the geometric connections of different minima through the lens of mode connectivity, which measures whether two minima can be connected with a low-loss path. |
| Outcome: | The proposed model can be used to find low-loss paths between two minima, and to understand how their mode connectivity affects their task knowledge. |
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents (2024.acl-long)
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Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng, Yujia Qin, Xin Cong, Zhong Zhang, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Current language model-driven agents lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. |
| Approach: | They propose a benchmark to inspect users’ implicit intentions through explicit queries and a model expert as the upstream in agent design to enhance user-agent interaction. |
| Outcome: | The proposed approach excels at identifying vague user tasks, recovering and summarizing critical missing information, setting precise and necessary agent execution goals, and minimizing redundant tool usage, thus boosting overall efficiency. |
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. |
CogToM: A Comprehensive Theory of Mind Benchmark inspired by Human Cognition for Large Language Models (2026.acl-long)
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Haibo Tong, Zeyang Yue, Feifei Zhao, Erliang Lin, Lu Jia, Ruolin Chen, Yinqian Sun, Qian Zhang, Yi Zeng
| Challenge: | Existing benchmarks for Large Language Models (LLMs) are limited to false belief tasks, highlighting bottlenecks in specific dimensions. |
| Approach: | They propose a benchmark to evaluate Large Language Models' Theory of Mind capabilities . they evaluate 8000 bilingual instances across 46 paradigms and validated by 49 human annotators . |
| Outcome: | The proposed benchmark reveals performance heterogeneities and bottlenecks in 22 representative models. |
CSMCIR: CoT-Enhanced Symmetric Alignment with Memory Bank for Composed Image Retrieval (2026.findings-acl)
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Zhipeng Qian, Zihan Liang, Yufei Ma, Ben Chen, Huangyu Dai, Yiwei Ma, Jiayi Ji, Chenyi Lei, Han Li, Xiaoshuai Sun
| Challenge: | Existing approaches to search for images using single-modality are limited by representation space fragmentation. |
| Approach: | They propose a unified representation framework that achieves efficient query-target alignment . they introduce a multi-level Chain-of-Thought prompting strategy that guides MLMs to generate discriminative, semantically compatible captions for target images . |
| Outcome: | The proposed framework achieves efficient query-target alignment through synergistic components. |
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning (2025.emnlp-main)
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Yu Sun, Xingyu Qian, Weiwen Xu, Hao Zhang, Chenghao Xiao, Long Li, Deli Zhao, Wenbing Huang, Tingyang Xu, Qifeng Bai, Yu Rong
| Challenge: | Existing medical reasoning datasets are limited in scale and typically rely on incomplete data. |
| Approach: | They propose to use ReasonMed to train medical reasoning models using a multi-agent generation, verification, and refinement pipeline. |
| Outcome: | The largest medical reasoning dataset to date surpasses the prior best sub-10B models by 4.17% and even exceeds LLaMA3.1-70B on PubMedQA by 4.60%. |
Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have emerged as powerful tools for a wide range of tasks, from * Equal Contribution. |
| Approach: | They propose a framework that enhances communication efficiency and task effectiveness in LLM-based multi-agent systems through training. |
| Outcome: | The proposed framework improves communication efficiency and task effectiveness on multi-agent tasks with 2.8x performance gain with less than 10% tokens on tasks requiring heavy information exchange. |
TruthReader: Towards Trustworthy Document Assistant Chatbot with Reliable Attribution (2024.emnlp-demo)
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| Challenge: | Document assistant chatbots are empowered with extensive capabilities by Large Language Models (LLMs) however, they suffer from hallucinations that are difficult to verify in the context of given documents. |
| Approach: | They propose a document assistant chatbot with reliable attribution that enables users to seek relevant information from given documents. |
| Outcome: | The proposed system generates answers with detailed inline citations, which can be attributed to the original document paragraphs, facilitating verification of factual consistency of the generated text. |
Towards Understanding Gender Bias in Relation Extraction (2020.acl-main)
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Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
| Challenge: | Existing bias mitigation techniques have a negative effect on NRE, a study finds . |
| Approach: | They create a dataset to analyze gender bias in relation extraction systems . they find that existing bias mitigation techniques have a negative effect on NRE . |
| Outcome: | The proposed dataset analyzes gender bias in relation extraction systems using a 10% human annotated test set. |
ProgressLM: Towards Progress Reasoning in Vision-Language Models (2026.acl-long)
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| Challenge: | Existing models for task progress estimation lack long-horizon and dynamic reasoning . estimating how much of a task has been completed requires long-term reasoning based on partial information. |
| Approach: | They propose a benchmark for evaluating progress reasoning from a single observation . they instantiate a two-stage paradigm that combines episodic retrieval with mental simulation . |
| Outcome: | The proposed benchmark improves on 14 VLMs on a small scale and shows common failure patterns. |
Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication (2024.findings-emnlp)
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Weize Chen, Chenfei Yuan, Jiarui Yuan, Yusheng Su, Chen Qian, Cheng Yang, Ruobing Xie, Zhiyuan Liu, Maosong Sun
| Challenge: | Natural language (NL) has long been the predominant format for human cognition and communication, but its utility in LLMs has not been thoroughly examined. |
| Approach: | They propose to allow LLMs to choose the most suitable format before reasoning or communicating, and to automate the selection process. |
| Outcome: | The proposed format improves reasoning efficiency and reduces token usage while maintaining communicative effectiveness. |
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)
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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang
| Challenge: | Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. |
| Approach: | AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. |
| Outcome: | Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% . |
AnyTrans: Translate AnyText in the Image with Large Scale Models (2024.findings-emnlp)
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| Challenge: | Recent advances in natural language processing and computer vision have made it possible to translate images with text in one language into equivalent images displaying that text translated into another language. |
| Approach: | They propose an all-encompassing framework for the task–In-Image Machine Translation (IIMT) that incorporates contextual cues from both textual and visual elements during translation. |
| Outcome: | The proposed framework can be constructed using open-source models and requires no training, making it highly accessible and expandable. |
BBTv2: Towards a Gradient-Free Future with Large Language Models (2022.emnlp-main)
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| Challenge: | Recent work on parameter-efficient tuning (PET) only tunes a small portion of parameters while keeping most of the parameters of the LLM unchanged. |
| Approach: | They propose an improved version of Black-Box Tuning to tune PTMs through gradient descent . they prepend continuous prompts to every layer of the PTM and propose a divide-and-conquer gradient-free algorithm to optimize the prompts alternately. |
| Outcome: | The proposed method achieves comparable performance to full model tuning and state-of-the-art parameter-efficient methods under few-shot settings while maintaining much fewer tunable parameters. |
Evaluating Large Language Models on Controlled Generation Tasks (2023.emnlp-main)
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Jiao Sun, Yufei Tian, Wangchunshu Zhou, Nan Xu, Qian Hu, Rahul Gupta, John Wieting, Nanyun Peng, Xuezhe Ma
| Challenge: | Recent studies have looked into the ability of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc. However, few studies investigate the controllability of large languages. |
| Approach: | They propose to compare large language models with state-of-the-start finetuned smaller models to find that large language model controls are comparable to smaller models. |
| Outcome: | The proposed model can meet hard constraints and perform better than state-of-the-art models. |
EoT: Evolution of Thoughts for Complex Reasoning Tasks (2025.findings-emnlp)
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| Challenge: | Existing studies focus on ensuring behavior fidelity, factuality or reliability in generated reasoning processes, but they neglect the simultaneous optimization of all three aspects for each thought. |
| Approach: | They propose a thought assessment method that is sensitive to knowledge and LLM behaviors . they use three scorers to evaluate each thought by considering domain context, semantic alignment, and behavior impact. |
| Outcome: | The proposed framework outperforms advanced approaches in knowledge-based complex tasks. |
The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning (2025.findings-acl)
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Bingxiang He, Ning Ding, Cheng Qian, Jia Deng, Ganqu Cui, Lifan Yuan, Haiwen Hong, Huan-ang Gao, Longtao Huang, Hui Xue, Huimin Chen, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing work on instruction tuning has focused on task level, without considering that tasks are artificially defined and, to LLMs, merely consist of tokens and representations. |
| Approach: | They propose a training data arrangement framework that allows for continual learning and loss reduction. |
| Outcome: | The proposed framework promotes continual learning and loss reduction on unseen tasks. |
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision (2021.acl-long)
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Si Sun, Yingzhuo Qian, Zhenghao Liu, Chenyan Xiong, Kaitao Zhang, Jie Bao, Zhiyuan Liu, Paul Bennett
| Challenge: | Neural information retrieval models have shown advanced results in many ranking scenarios where massive relevance labels or clickthrough data are available. |
| Approach: | They propose a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains. |
| Outcome: | The proposed method improves the few-shot ranking accuracy of Neu-IR models on three TREC benchmarks in the web, news, and biomedical domains. |
UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents (2026.acl-long)
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Yifan Ji, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Qian Zhang, Zhibo Yang, Junyang Lin, Yu Gu, Ge Yu, Maosong Sun
| Challenge: | Recent Large Multimodal Models (LMMs) have shown promising potential for performing end-to-end KIE directly from document images. |
| Approach: | They propose a benchmark to evaluate the performance of Large Multimodal Models (LMMs) using a constrained-category KIE track and an open-categorical KIE Track. |
| Outcome: | Experiments on 15 state-of-the-art LMMs show performance degradation under diverse schema definitions, long-tail key fields, and complex layouts, along with pronounced performance disparities across different document types and scenarios. |
Dynamic Open-book Prompt for Conversational Recommender System (2023.findings-emnlp)
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| Challenge: | Existing methods for prompt learning use only training samples for parameter training, limiting the performance of existing methods. |
| Approach: | They propose a Dynamic Open-book Prompt approach where the open book stores user's experiences in historical data and dynamically constructs the prompt to memorize the user' s current utterance. |
| Outcome: | The proposed model improves on the existing methods on the ReDial dataset and shows that it can be used to learn contextually relevant recommendations. |
Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering (2025.acl-long)
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Shuzheng Si, Haozhe Zhao, Gang Chen, Cheng Gao, Yuzhuo Bai, Zhitong Wang, Kaikai An, Kangyang Luo, Chen Qian, Fanchao Qi, Baobao Chang, Maosong Sun
| Challenge: | Existing studies show that training LLMs on data containing unfamiliar knowledge during instruction tuning can encourage hallucinations. |
| Approach: | They propose a framework that measures how familiar the LLM is with instruction data and introduce an expert-aligned reward model to ensure the quality of selected samples. |
| Outcome: | The proposed framework reduces hallucinations while maintaining a competitive ability to follow instructions. |
Adaption-of-Thought: Learning Question Difficulty Improves Large Language Models for Reasoning (2024.emnlp-main)
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| Challenge: | Existing methods do not differentiate question difficulty when designing prompting methods for them. |
| Approach: | They propose an adaptive method to improve large language models for reasoning problems by measuring question difficulty and tailoring demonstration set construction and difficulty-adapted retrieval strategies. |
| Outcome: | The proposed method shows an absolute improvement of up to 5.5% on arithmetic reasoning, 7.4% on symbolic reasoning, and 2.3% on commonsense reasoning. |
Toward Informal Language Processing: Knowledge of Slang in Large Language Models (2024.naacl-long)
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| Challenge: | Recent advances in large language models (LLMs) have offered a strong potential for natural language systems to process informal language. |
| Approach: | They propose to use movie subtitles to evaluate slang in large language models . they find that smaller LLMs finetuned on the dataset achieve comparable performance . |
| Outcome: | The proposed dataset can be used to evaluate LLMs on slang detection and identification of regional and historical sources for interpretive insights. |
Experiential Co-Learning of Software-Developing Agents (2024.acl-long)
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Chen Qian, Yufan Dang, Jiahao Li, Wei Liu, Zihao Xie, YiFei Wang, Weize Chen, Cheng Yang, Xin Cong, Xiaoyin Che, Zhiyuan Liu, Maosong Sun
| Challenge: | Recent advances in large language models (LLMs) have brought significant changes to various domains, especially through autonomous agents. |
| Approach: | They propose a framework that lets agents learn shortcuts from their past tasks and use them for future task execution. |
| Outcome: | The proposed framework enables agents to tackle unseen software-developing tasks more effectively. |
Equal Truth: Rumor Detection with Invariant Group Fairness (2025.findings-emnlp)
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| Challenge: | Existing rumor detection methods rarely consider fairness issues inherent in the model . this can lead to biased predictions across stakeholder groups, undermining their detection effectiveness . |
| Approach: | They propose a framework to address fairness issues inherent in rumor detection models . they perform unsupervised partitioning to dynamically identify potential unfair data patterns . then, they apply invariant learning to these partitions to extract fair and informative feature representations . |
| Outcome: | The proposed method outperforms strong baselines regarding detection and fairness performance . it also shows robust performance on out-of-distribution samples . |
Beyond Sentence-level Labels: Integrating Conversational Context and Personal Experience for Natural Emotional Expression (2026.findings-acl)
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Haiyang Sun, Chenyang Le, Wei Wang, Leying Zhang, Chuang Li, Bing Han, Chenda Li, Mengxiao Bi, Yanmin Qian
| Challenge: | Existing systems rely on sentence-level labels, which fails to capture the subtle nuances of human affect. |
| Approach: | They propose to use a large-scale, context-aware speech corpus derived from multi-speaker audiobooks to generate a speech that is human-like. |
| Outcome: | The proposed model outperforms existing methods in terms of emotional expression accuracy and naturalness. |
Surge: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors (2025.emnlp-main)
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| Challenge: | Neural surrogate models are powerful tools in data mining, but are underexplored . large language models (LLMs) have demonstrated remarkable capabilities in code-related tasks . |
| Approach: | They propose a benchmarking framework to examine the feasibility of large language models . they examine scaling laws, data efficiency, and predictive accuracy of 21 open-source and proprietary LLMs . |
| Outcome: | The proposed benchmark examines 21 open-source and proprietary LLMs . it also examines scaling laws, data efficiency, and predictive accuracy . |
Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)
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Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing approaches lack flexibility to address diverse and ever-evolving user queries in open domains. |
| Approach: | They propose to evaluate LLMs on open-domain knowledge that requires tools to solve diverse and ever-evolving user queries. |
| Outcome: | The proposed system outperforms baselines in the open domain task-solving benchmark. |
Active Retrieval Augmented Generation (2023.emnlp-main)
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Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig
| Challenge: | Generative language models (LMs) have a tendency to hallucinate and create inaccurate output. |
| Approach: | They propose a method which iteratively uses a prediction of the upcoming sentence to anticipate future content. |
| Outcome: | The proposed method achieves superior or competitive performance on all tasks . iteratively uses a prediction of the upcoming sentence to anticipate future content . |