Papers by Qian Fu
Reasoning Like Program Executors (2022.emnlp-main)
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Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Qiang Fu, Yan Gao, Jian-Guang Lou, Weizhu Chen
| Challenge: | Existing language models are inadequate in reasoning, according to studies . a new reasoning pre-training paradigm is based on pretraining language models with programs . |
| Approach: | They propose a reasoning pre-training paradigm that empowers language models to harvest reasoning knowledge possessed by program executors. |
| Outcome: | The proposed reasoning pre-training paradigm can boost models' reasoning skills . it can be instantiated by different kinds of program executors and run on a single database . |
Duplicate-Aware Controlled Code Generation: Enhancing Copyright Protection with Targeted Reordering Beam Search in LLMs (2026.findings-acl)
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| Challenge: | Experimental results demonstrate that TRBS effectively reduces verbatim repetition while maintaining functional adequacy. |
| Approach: | They propose a plug-and-play decoding method that dynamically reorders beam candidates to reduce direct copying. |
| Outcome: | The proposed method reduces verbatim repetition while maintaining functional adequacy on a multi-language code generation benchmark. |
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% . |
AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models (2026.findings-acl)
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Yimin Deng, Yejing Wang, Zhenxi Lin, Zichuan Fu, Guoshuai Zhao, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Xian Wu, Li Zhu, Xueming Qian
| Challenge: | Existing methods for temporal reasoning are limited and apply a fixed pipeline to all questions. |
| Approach: | They propose an adaptive temporal reasoning method that dynamically executes reasoning steps based on context and task requirements. |
| Outcome: | Experiments on two temporal QA benchmarks show the proposed method works. |
A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs (2025.findings-acl)
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Yimin Deng, Yuxia Wu, Yejing Wang, Guoshuai Zhao, Li Zhu, Qidong Liu, Derong Xu, Zichuan Fu, Xian Wu, Yefeng Zheng, Xiangyu Zhao, Xueming Qian
| Challenge: | Existing methods focus on graph structure learning or semantic reasoning, lacking the capability to capture the inherent differences between historical and non-historical events. |
| Approach: | They propose a temporal knowledge graph reasoning framework that integrates both structural and semantic information to guide the reasoning process for different events. |
| Outcome: | The proposed framework integrates structural and semantic information to predict future events . it can provide evidence for many downstream tasks, including situation analysis and political decision making . |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
COAS2W: A Chinese Older-Adults Spoken-to-Written Transformation Corpus with Context Awareness (2025.emnlp-main)
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| Challenge: | Existing work on spoken-to-written transformations from older adults' language is limited by omission, disordered syntax, constituent errors, and redundancy. |
| Approach: | They propose to combine a spoken-to-written corpus of 10,004 utterances from older adults with a written version, fine-grained error labels, and four-sentence context. |
| Outcome: | The proposed model outperforms closed-source models on Chinese spoken-to-written corpus and shows that multi-sentence input is more efficient. |
Learning to Paraphrase for Alignment with LLM Preference (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) exhibit the issue of paraphrase divergence, which means that when a question is phrased in a slightly different but semantically similar way, LLM may output a wrong response . retraining faces challenges in meeting the computational costs and privacy security demands of LLMs. |
| Approach: | They propose a black-box method that enhances model performance by paraphrasing questions in expressions preferred by the model. |
| Outcome: | The proposed method improves performance by paraphrasing questions in expressions preferred by the model. |
Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs (2025.findings-acl)
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Runchu Tian, Yanghao Li, Yuepeng Fu, Siyang Deng, Qinyu Luo, Cheng Qian, Shuo Wang, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Huadong Wang, Xiaojiang Liu
| Challenge: | Positional biases in large language models hinder their ability to process long inputs. |
| Approach: | They propose a benchmark to assess positional bias in large language models involving multiple pieces of relevant information. |
| Outcome: | The proposed benchmark assesses the performance of long-context language models by examining their models with different input lengths and tasks. |
MMAPG: A Training-Free Framework for Multimodal Multi-hop Question Answering via Adaptive Planning Graphs (2025.emnlp-main)
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| Challenge: | Existing multimodal question answering models rely on sequential retrieval and reasoning, but this single-path paradigm makes them vulnerable to errors due to misleading intermediate steps. |
| Approach: | They propose a multimodal multi-hop question answering framework guided by an Adaptive Planning Graph . they propose modality-specific strategies that dynamically adapt to distinct data types . |
| Outcome: | The proposed framework outperforms existing models that rely on training. |
MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning (2026.findings-acl)
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Yimin Deng, Zhenxi Lin, Yejing Wang, Guoshuai Zhao, Pengyue Jia, Zichuan Fu, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Li Zhu, Xian Wu, Xueming Qian
| Challenge: | Existing approaches focus on diagnostic reasoning based on internal model knowledge or static knowledge bases. |
| Approach: | They propose a two-stage diagnostic reasoning framework that integrates multi-perspective evidence to generate a diagnostic prediction. |
| Outcome: | The proposed method generates suspected diagnoses and reasoning traces from web search, SOAP-formatted case, and clinical case database. |
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. |
Improving Text Generation with Student-Forcing Optimal Transport (2020.emnlp-main)
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Jianqiao Li, Chunyuan Li, Guoyin Wang, Hao Fu, Yuhchen Lin, Liqun Chen, Yizhe Zhang, Chenyang Tao, Ruiyi Zhang, Wenlin Wang, Dinghan Shen, Qian Yang, Lawrence Carin
| Challenge: | Maximum likelihood estimation (MLE) is used to train models, but during testing, the model is conditioned on previously generated tokens, resulting in exposure bias. |
| Approach: | They propose to use optimal transport to match the sequences generated in MLE and test modes to reduce exposure bias. |
| Outcome: | The proposed method is validated on machine translation, text summarization, and text generation tasks. |
TeachMaster: Generative Teaching via Code (2026.acl-industry)
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Yuheng Wang, Runde Yang, Lin Wu, Jie Zhang, Jingru Fan, Tianle Zhou, Ruoyu Fu, Huatao Li, Ruijie Shi, Siheng Chen, Weinan E, Chen Qian
| Challenge: | Existing methods for creating video content are limited by high costs and slow update cycles. |
| Approach: | They propose a paradigm shifting educators from manual creators to high-level directors who focus on pedagogical intents while agents handle execution. |
| Outcome: | The proposed framework reduces production costs to 0.3% of traditional course videos and provides a robust solution for scalable education. |
ISACL: Internal State Analyzer for Copyrighted Training Data Leakage (2025.findings-emnlp)
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| Challenge: | Traditional methods address leaks only after content is generated, which can lead to the exposure of sensitive information. |
| Approach: | They propose a proactive approach: examining LLMs’ internal states before text generation to detect potential leaks. |
| Outcome: | The proposed framework ensures adherence to copyright and licensing requirements while enhancing data privacy and ethical standards. |