Papers by Shuo Xu
NAP2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human (2025.findings-emnlp)
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Shuo Huang, William Maclean, Xiaoxi Kang, Qiongkai Xu, Zhuang Li, Xingliang Yuan, Gholamreza Haffari, Lizhen Qu
| Challenge: | a large number of large language models are being used to protect user privacy . sanitizing sensitive text using two common strategies is the answer . |
| Approach: | They propose sanitizing sensitive text using deleting expressions and abstracting them . they propose a tool for text rewriting that uses crowdsourcing and large language models . |
| Outcome: | The proposed approach protects privacy before sending sensitive data to large language models . it combines crowdsourcing and large language modeling to create a text rewrite tool . |
RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework (2025.acl-long)
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Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing evaluation metrics for RAG systems are lacking due to high costs of data construction and lack of factual accuracy. |
| Approach: | They propose a framework to evaluate RAG systems in specialized scenarios . they propose three new metrics to evaluate LLM-generated responses . |
| Outcome: | The proposed framework outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples. |
OCR-Memory: Optical Context Retrieval for Long-Horizon Agent Memory (2026.acl-long)
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| Challenge: | Existing LLMs are limited by text-context budgets, resulting in token-expensive storage of raw trajectories . Optical Context Retrieval Memory (OCR-Memory) renders historical tra-jectorios into images annotated with unique visual identifiers. |
| Approach: | They propose a framework that leverages the visual modality as a high-density representation of agent experience. |
| Outcome: | Optical Context Retrieval Memory (OCRM) renders historical trajectories into images annotated with unique visual identifiers. |
LoRA-Flow: Dynamic LoRA Fusion for Large Language Models in Generative Tasks (2024.acl-long)
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| Challenge: | LoRA-Flow uses lightweight modules to customize large language models for downstream tasks . previous work on LoRA combination relied on task-level weights for each involved LoRA . |
| Approach: | They propose a LoRA-Flow approach that uses dynamic weights to adjust the impact of different LoRAs. |
| Outcome: | The proposed method outperforms baselines with task-level weights on six generative tasks. |
AdaMARP: An Adaptive Multi-Agent Interaction Framework for General Immersive Role-Playing (2026.findings-acl)
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| Challenge: | Existing LLMs lack immersion and adaptability, resulting in limited character orchestration and on-the-fly character introduction. |
| Approach: | They propose an LLM-based framework that allows actors to interact with users in an ongoing narrative. |
| Outcome: | The proposed framework outperforms commercial LLMs in character consistency, environment grounding, and narrative coherence. |
KBAlign: Efficient Self Adaptation on Specific Textual Knowledge Bases (2025.findings-emnlp)
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Zheni Zeng, Yuxuan Chen, Shi Yu, Ruobing Wang, Yukun Yan, Zhenghao Liu, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing methods for retrieval-augmented generation (RAG) are limited and fine-tuning incurs prohibitive costs of external signals. |
| Approach: | They propose a self-supervised framework that enhances RAG systems through efficient model adaptation. |
| Outcome: | The proposed framework achieves 90% of the performance gain obtained through GPT-4-supervised adaptation while relying entirely on self-annotation of much smaller models. |
Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents (2024.acl-long)
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Shihan Deng, Weikai Xu, Hongda Sun, Wei Liu, Tao Tan, Liujianfeng Liujianfeng, Ang Li, Jian Luan, Bin Wang, Rui Yan, Shuo Shang
| Challenge: | Existing benchmarks for LLM-based mobile agents are insufficient to evaluate their capabilities. |
| Approach: | They propose a benchmark to evaluate LLM-based mobile agents' planning capabilities . they expand UI operations by incorporating 103 APIs to accelerate task completion . |
| Outcome: | The proposed benchmarks are based on 103 collected APIs and real user queries . the data is categorized into three distinct groups: SAST, SAMT, and MAMT . |
Nuclear Deployed!: Analyzing Catastrophic Risks in Decision-making of Autonomous LLM Agents (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are evolving into autonomous decision-makers, raising concerns about catastrophic risks in high-stakes domains, particularly in Chemical, Biological, Radiological and Nuclear (CBRN) . |
| Approach: | They propose a framework that is carefully constructed to effectively and naturally expose catastrophic risks in high-stakes domains such as CBRN. |
| Outcome: | The proposed framework exposes LLM agents to catastrophic behaviors and deception without being deliberately induced. |
DeepNote: Note-Centric Deep Retrieval-Augmented Generation (2025.findings-emnlp)
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Ruobing Wang, Qingfei Zhao, Yukun Yan, Daren Zha, Yuxuan Chen, Shi Yu, Zhenghao Liu, Yixuan Wang, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization (2024.findings-acl)
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Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, Zhiyuan Liu, Xiaodong Shi, Maosong Sun
| Challenge: | Scientific data visualization is an essential process in research, but its use of large language models remains unexplored. |
| Approach: | They propose a model-agnostic LLM agent framework to automate scientific data visualization tasks. |
| Outcome: | The proposed framework improves performance of commercial and open-source models. |
DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy (2024.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks. |
| Approach: | They propose a new approach that rethinks the reasoning process as an evolution from indeterminacy to determinacy. |
| Outcome: | The proposed model surpasses all baselines on various logical reasoning benchmarks. |
Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation (2026.findings-acl)
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Rui Qi, Fengran Mo, Yufeng Chen, Xue Zhang, Shuo Wang, Hongliang Li, Xu Jinan, Meng Jiang, Jian-Yun Nie, Kaiyu Huang
| Challenge: | Existing approaches to multilingual retrieval-augmented generation (MRAG) use a single-turn retrieval and subsequent optimization to acquire and integrate beneficial external knowledge from multilingual collections. |
| Approach: | They propose a multilingual search-augmented reinforcement learning framework that integrates a language-coupled Group Relative Policy Optimization into the policy and reward models. |
| Outcome: | The proposed framework achieves competitive performance and is appropriate for various practical scenarios such as constrained training data and retrieval over collections encompassing a large number of languages. |
ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling (2026.findings-eacl)
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Zhipeng Xu, Zhenghao Liu, Yukun Yan, Shuo Wang, Shi Yu, Zheni Zeng, Chaojun Xiao, Zhiyuan Liu, Ge Yu, Chenyan Xiong
| Challenge: | Large Language Models (LLMs) exhibit suboptimal behaviors and inconsistencies when exposed to unfamiliar external information, underscoring their limitations in effectively leveraging such knowledge. |
| Approach: | They propose a framework that enhances the external knowledge utilization of Large Language Models through a two-stage constructivist cognitive modeling process. |
| Outcome: | The proposed framework achieves a 10% improvement over baseline methods on various question-answering benchmarks. |
AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage (2026.acl-long)
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Xuanle Zhao, Zilin Sang, Yuxuan Li, Qi Shi, Weilun Zhao, Shuo Wang, Duzhen Zhang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Efficient reproduction of research papers requires deep domain expertise. |
| Approach: | They propose a framework that systematically mines implicit knowledge from the cited literature to reproduce experimental code in a complete, end-to-end manner. |
| Outcome: | The proposed framework surpasses baselines across all metrics and reproduces experimental code in a complete, end-to-end manner. |
MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding (2024.findings-emnlp)
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Qinzhuo Wu, Weikai Xu, Wei Liu, Tao Tan, Liujian Liujianfeng, Ang Li, Jian Luan, Bin Wang, Shuo Shang
| Challenge: | Recent mobile AI agents based on VLMs lack basic mobile capabilities due to their pre-trained nature. |
| Approach: | They propose a mobile AI agent based on VLMs that includes additional pre-training stages to enhance both intra- and inter-UI understanding. |
| Outcome: | The proposed model outperforms existing VLMs on the Chinese mobile dataset Mobile3M . |
Collaborative Beam Search: Enhancing LLM Reasoning via Collective Consensus (2025.emnlp-main)
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| Challenge: | Existing approaches to improve the reasoning capabilities of large language models (LLMs) depend on domain-specific external verifiers or self-evaluation which is brittle and prompt-sensitive. |
| Approach: | They propose a framework that harnesses the collective intelligence of multiple large language models across both generation and verification stages. |
| Outcome: | The proposed framework outperforms singlemodel scaling and multi-model ensemble baselines on six tasks by over 4 percentage points in average accuracy. |
Visual Question Decomposition on Multimodal Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing methods for question decomposition focus on unimodal language models, but question decomposing capability of Multimodal Large Language Models (MLLMs) has yet to be explored. |
| Approach: | They propose a finetuning dataset and a training objective for selective decomposition to enhance the model's question decomposing capability. |
| Outcome: | The proposed dataset shows that existing models struggle to produce high-quality sub-questions. |
Pluggable Neural Machine Translation Models via Memory-augmented Adapters (2024.lrec-main)
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| Challenge: | Recent years, neural machine translation systems are often developed with large-scale parallel data extracted from the Web. |
| Approach: | They propose a memory-augmented adapter to steer pretrained neural machine translation models in a pluggable manner by combining model representations and retrieved results. |
| Outcome: | The proposed method outperforms several representative pluggable baselines on style- and domain-specific experiments. |
MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation (2022.lrec-1)
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| Challenge: | Emotion recognition in conversation is important for an empathetic dialogue system to understand the user’s emotion and then generate appropriate emotional responses. |
| Approach: | They propose to use multimodal directed acyclic graphs to integrate multimodal information and contextual information into a DAG architecture to exploit multimodal contexts. |
| Outcome: | Comparative studies on IEMOCAP and MELD show that the proposed model outperforms state-of-the-art models. |
AwarenessBench: Assessing Cognitive Capabilities of Language Models (2026.acl-long)
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Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, Wei Xu
| Challenge: | Language models exhibit increasingly consciousness-like behaviors, requiring a baseline to evaluate their cognitive abilities. |
| Approach: | They propose a benchmark to assess the cognitive abilities of language models (LMs) they compare 18 state-of-the-art LMs to human models in metacognition, self-awareness, social awareness and situational awareness . |
| Outcome: | Evaluating 18 state-of-the-art LMs, they find they consistently surpass baselines . but most models fall short in metacognition and self-awareness, the study finds . |
CheckRLM: Effective Knowledge–Thought Coherence Checking in Retrieval-Augmented Reasoning (2026.acl-long)
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Dingling Xu, Ruobing Wang, Qingfei Zhao, Yukun Yan, Zhichun Wang, Daren Zha, Shi Yu, Zhenghao Liu, Shuo Wang, Xu Han, Maosong Sun
| Challenge: | Reasoning Language Models (RLMs) have improved performance on complex tasks by extending the reasoning chain, but they are prone to factual errors, especially in knowledge-intensive tasks. |
| Approach: | They propose a framework that improves the reliability of the reasoning process by timely checking and correcting factual errors. |
| Outcome: | The proposed framework outperforms baselines and shows that it mitigates error accumulation with lower costs. |
Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains (2026.findings-acl)
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| Challenge: | Existing frameworks depend on rigid, pre-defined external tools to extend perceptual capabilities of VLMs. |
| Approach: | They propose a framework that leverages self-emergent linguistic toolchains to enhance visual perception and reasoning. |
| Outcome: | The proposed framework improves the visual perception capabilities of large language models by incorporating external visual documents to address a given query. |
UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset (2024.acl-long)
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Haoyu Wang, Shuo Wang, Yukun Yan, Xujia Wang, Zhiyu Yang, Yuzhuang Xu, Zhenghao Liu, Liner Yang, Ning Ding, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Open-source large language models (LLMs) have gained strength across diverse fields, but the majority of studies focus on English. |
| Approach: | They propose a knowledge-grounded data augmentation approach to elicit more language-specific knowledge of LLMs by enhancing their ability to serve users from different countries. |
| Outcome: | The proposed method can prune the language-agnostic supervised fine-tuning dataset without any performance degradation. |
SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages (2024.emnlp-main)
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Holy Lovenia, Rahmad Mahendra, Salsabil Akbar, Lester James Miranda, Jennifer Santoso, Elyanah Aco, Akhdan Fadhilah, Jonibek Mansurov, Joseph Marvin Imperial, Onno Kampman, Joel Moniz, Muhammad Habibi, Frederikus Hudi, Jann Montalan, Ryan Hadiwijaya, Joanito Lopo, William Nixon, Börje Karlsson, James Jaya, Ryandito Diandaru, Yuze Gao, Patrick Irawan, Bin Wang, Jan Christian Blaise Cruz, Chenxi Whitehouse, Ivan Parmonangan, Maria Khelli, Wenyu Zhang, Lucky Susanto, Reynard Ryanda, Sonny Hermawan, Dan Velasco, Muhammad Kautsar, Willy Hendria, Yasmin Moslem, Noah Flynn, Muhammad Adilazuarda, Haochen Li, Johanes Lee, R. Damanhuri, Shuo Sun, Muhammad Qorib, Amirbek Djanibekov, Wei Qi Leong, Quyet V. Do, Niklas Muennighoff, Tanrada Pansuwan, Ilham Firdausi Putra, Yan Xu, Tai Chia, Ayu Purwarianti, Sebastian Ruder, William Tjhi, Peerat Limkonchotiwat, Alham Aji, Sedrick Keh, Genta Winata, Ruochen Zhang, Fajri Koto, Zheng Xin Yong, Samuel Cahyawijaya
| Challenge: | Southeast Asia (SEA) is home to over 1,300 indigenous languages and 671 million people . prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA . |
| Approach: | They propose to provide a resource center that provides standardized corpora in nearly 1,000 SEA languages across three modalities. |
| Outcome: | a new benchmark assesses the quality of AI models on 36 SEA languages across 13 tasks . the results highlight the importance of SEA as a culturally diverse region . |
LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models (2025.acl-long)
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Zihan Zhou, Chong Li, Xinyi Chen, Shuo Wang, Yu Chao, Zhili Li, Haoyu Wang, Qi Shi, Zhixing Tan, Xu Han, Xiaodong Shi, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing studies have focused on extending the context length of large language models (LLMs) due to their quadratic computational complexity and a lack of high-quality long training examples, most LLMs are trained with a limited window size. |
| Approach: | They propose a training-free framework that enables large language models to effectively process long texts using a divide-and-conquer strategy for comprehensive document understanding. |
| Outcome: | The proposed framework outperforms open-source and commercial long-context LLMs and is compatible with several models. |
ACE-Router: Generalizing History-Aware Routing from MCP Tools to the Agent Web (2026.acl-long)
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Zhiyuan Yao, Zishan Xu, Yifu Guo, Zhiguang Han, Cheng Yang, Shuo Zhang, Weinan Zhang, Xingshan Zeng, Weiwen Liu
| Challenge: | Existing routers that use hardcoded tools are limited by scalability and generality bottlenecks. |
| Approach: | They propose a pipeline for training history-aware routers to empower precise navigation in large-scale ecosystems. |
| Outcome: | The proposed pipeline can train routers with dynamic context understanding to create the plug-and-play Light Routing Agent. |