Papers by Fan Yuan
ToolSafety: A Comprehensive Dataset for Enhancing Safety in LLM-Based Agent Tool Invocations (2025.emnlp-main)
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| Challenge: | Current models exhibit notable vulnerabilities in maintaining safety during multi-step tool interactions and in indirect harm scenarios. |
| Approach: | They propose a safety fine-tuning dataset to fine- tune LLMs into assistants . they propose to use synthesized trajectories and realistic, context-aware sample generation . |
| Outcome: | The proposed model maintains safety in multi-step and indirect harm scenarios with little impact on helpfulness. |
1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators? (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have been recognized for their impressive capabilities in natural language processing (NLP). |
| Approach: | They propose a method to enhance the multilingual performance of Large Language Models by aggregating knowledge from diverse languages. |
| Outcome: | The proposed method reduces the performance disparity across languages and offers valuable insights for further exploration. |
HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding (2024.emnlp-main)
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| Challenge: | Existing work detects hallucination by directly judging whether an object exists in an image, overlooking the association between the object and semantics. |
| Approach: | They propose a framework that incorporates hallucination feedback at both object and sentence semantic levels to alleviate over 15% of hallucinism. |
| Outcome: | The proposed framework can alleviate over 15% of hallucination even with a marginal degree of training. |
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. |
Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction (2020.acl-main)
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| Challenge: | Existing methods to extract emotions and causes from unannotated text are pipelined, causing error propagation. |
| Approach: | They propose to transform a task into a procedure of parsing-like directed graph construction . they propose to generate a directed graph with labeled edges based on a sequence of actions . |
| Outcome: | The proposed method outperforms the state-of-the-art methods by 6.71% (p0.01) in F1 measure. |
RBPtool: A Deep Language Model Framework for Multi-Resolution RBP-RNA Binding Prediction and RNA Molecule Design (2025.emnlp-main)
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Jiyue Jiang, Yitao Xu, Zikang Wang, Yihan Ye, Yanruisheng Shao, Yuheng Shan, Jiuming Wang, Xiaodan Fan, Jiao Yuan, Yu Li
| Challenge: | RNA-binding proteins play key roles in post-transcriptional gene regulation . existing methods focus on shallow sequence features or coarse structural representations . large language models allow for precise modeling and biologically informed de novo RNA design . |
| Approach: | They extend RPI15223 into a multi-resolution, structure-level RBP-RNA dataset and introduce RBPtool, a framework that fuses sequence and structural information. |
| Outcome: | The proposed framework achieves state-of-the-art performance on public benchmarks and the RPI15223 dataset while supporting fine-grained level predictions. |
Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding (2026.acl-long)
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Da Li, Yuxiao Luo, Keping Bi, Jiafeng Guo, Wei Yuan, Biao Yang, Yan Wang, Fan Yang, Tingting Gao, Guorui Zhou
| Challenge: | Recent approaches demonstrate that MLLMs can be adapted into competitive embedding models via large-scale contrastive learning. |
| Approach: | They propose a compressed pre-training phase which serves as a warm-up stage for contrastive learning. |
| Outcome: | The proposed model achieves state-of-the-art among MLLMs of comparable size on the MMEB, realizing optimization in both efficiency and effectiveness. |
Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)
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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang
| Challenge: | Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. |
| Approach: | They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice. |
| Outcome: | The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models . |
Exploiting Contextual Knowledge in LLMs through đť’±-usable Information based Layer Enhancement (2025.acl-long)
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| Challenge: | Existing approaches to enhance the context-faithfulness of Large Language Models (LLMs) ignore the fundamental mechanism of how contextual information is processed within LLMs’ internal states. |
| Approach: | They propose a method that enhances the utilization of contextual knowledge within LLMs’ internal representations by employing V-usable information analysis. |
| Outcome: | The proposed method improves context-faithfulness generation in Question-Answering tasks, particularly in scenarios involving unknown or conflicting contextual knowledge. |
Structural Supervision for Word Alignment and Machine Translation (2022.findings-acl)
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| Challenge: | Existing knowledge on syntactic structure neglects the rich structural information from target tokens and the structural similarity between the source and target sentences. |
| Approach: | They propose to incorporate syntactic structure of both source and target tokens into the encoder-decoder framework, tightly correlating the internal logic of word alignment and machine translation for multi-task learning. |
| Outcome: | The proposed method outperforms baselines on four publicly available language pairs and consistently outperformed baselines in alignment accuracy and translation quality. |
On the Step Length Confounding in LLM Reasoning Data Selection (2026.findings-acl)
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Bing Wang, Rui Miao, Chen Shen, Shaotian Yan, Kaiyuan Liu, Ximing Li, Xiaosong Yuan, Sinan Fan, Jun Zhang, Jieping Ye
| Challenge: | Existing pipelines generate long reasoning data from more capable Large Language Models (LLMs) and apply manually heuristic or naturalness-based selection methods to filter high-quality samples. |
| Approach: | They propose to use supervised fine-tuning to generate long reasoning data from more capable Large Language Models and apply manually heuristic or naturalness-based selection methods to filter high-quality samples. |
| Outcome: | Experiments on four LLMs and five evaluation benchmarks show that the proposed approach is effective in mitigating step length confounding problem. |
FlowSearch: Advancing Deep Research with Dynamic Structured Knowledge Flow (2026.acl-long)
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Yusong Hu, Runmin Ma, Yue Fan, Jinxin Shi, Zongsheng Cao, Yuhao Zhou, Jiakang Yuan, Shuaiyu Zhang, Shiyang Feng, Xiangchao Yan, Shufei Zhang, Wenlong Zhang, Lei Bai, Bo Zhang
| Challenge: | FlowSearch is a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning. |
| Approach: | They propose a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning. |
| Outcome: | The proposed framework achieves competitive performance on GAIA, HLE, GPQA and TRQA benchmarks and is available to download. |
LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review (2025.acl-demo)
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| Challenge: | Large language models (LLMs) are capable of generating inaccurate discharge summary content or fabricating information without valid sources. |
| Approach: | They propose a tool for empowering LLMs with Logic-Controlled Discharge Summary generation. |
| Outcome: | The proposed tool identifies the writing logic of discharge summaries and integrates it with EMRs to generate silver discharge summararies. |
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)
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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang
| Challenge: | Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence. |
| Approach: | They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included . |
| Outcome: | The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model. |
SeedBench: A Multi-task Benchmark for Evaluating Large Language Models in Seed Science (2025.acl-long)
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Jie Ying, Zihong Chen, Zhefan Wang, Wanli Jiang, Chenyang Wang, Zhonghang Yuan, Haoyang Su, Huanjun Kong, Fan Yang, Nanqing Dong
| Challenge: | Seed science is essential for modern agriculture, but its application in seed science remains limited due to a shortage of experts and limited availability of online resources. |
| Approach: | They evaluate 26 leading large language models and compare them against a set of benchmarks . they find that there is a gap between the power of LLMs and real-world seed science problems . |
| Outcome: | The new seed benchmark highlights the gap between the power of large language models and real-world seed science problems. |
Entity-Relation Extraction as Multi-Turn Question Answering (P19-1)
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| Challenge: | Identifying entities and their relations is the prerequisite of extracting structured knowledge from unstructured raw texts. |
| Approach: | They propose a new paradigm for the task of entity-relation extraction . they cast the task as a multi-turn question answering problem . |
| Outcome: | The proposed paradigm significantly outperforms previous best models on the ACE and CoNLL04 datasets. |
Incorporating Object-Level Visual Context for Multimodal Fine-Grained Entity Typing (2023.findings-emnlp)
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| Challenge: | Experimental results show that fine-grained entity typing is superior to text-based methods. |
| Approach: | They propose a task called fine-grained entity typing to classify entities . they propose combining textual and visual contexts to capture fine-granular semantic information . |
| Outcome: | The proposed approach achieves superior classification performance compared to previous text-based approaches. |
Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme (2020.emnlp-main)
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| Challenge: | Existing methods to extract emotions and causes from unannotated emotion texts are labor intensive and limited applications in real-world scenarios. |
| Approach: | They propose a novel task to find emotions and corresponding causes in unannotated emotion texts. |
| Outcome: | The proposed model outperforms the state-of-the-art method by 2.26% (p0.001) in F1 measure. |
PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models (2024.acl-long)
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Haoran Li, Dadi Guo, Donghao Li, Wei Fan, Qi Hu, Xin Liu, Chunkit Chan, Duanyi Yao, Yuan Yao, Yangqiu Song
| Challenge: | generative large language models (LLMs) exhibit surprising capability and integrate previous tasks into a unified text generation formulation. |
| Approach: | They propose a privacy evaluation benchmark to quantify the privacy leakage of language models. |
| Outcome: | The proposed benchmark compares PPLMs with different privacy implementations to find out how privacy leakage is handled. |
RefGPT: Dialogue Generation of GPT, by GPT, and for GPT (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have impressive capability to resolve a wide range of NLP tasks by fine-tuning high-quality instruction data. |
| Approach: | They propose a method to generate huge truthful and customized dialogues without worrying about factual errors caused by the model hallucination. |
| Outcome: | The proposed method solves the model hallucination in dialogue generation by restricting the LLMs to leverage the given reference instead of reciting their own knowledge to generate dialogues. |