Papers by Qingyun Wang
MedEBench: Diagnosing Reliability in Text-Guided Medical Image Editing (2025.findings-emnlp)
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| Challenge: | Text-guided image editing has seen significant progress in natural image domains, but its application in medical imaging remains limited. |
| Approach: | a new benchmark is designed to diagnose reliability in text-guided medical image editing. a clinically grounded evaluation framework measures Editing Accuracy, Context Preservation, and Visual Quality. |
| Outcome: | a new benchmark is designed to diagnose reliability in medical image editing. |
LORE: Continual Logit Rewriting Fosters Faithful Generation (2025.findings-emnlp)
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| Challenge: | Using aspect-oriented summarization as a case study, we propose **LOgit REwriting**, a new controlled generation paradigm which can be faithful to external knowledge and to the LLM’s intentions. |
| Approach: | They propose a controlled generation paradigm which can be faithful to external knowledge and to the LLM's intentions. |
| Outcome: | The proposed paradigm can be faithful to external knowledge and to the LLM's intentions while balancing that with accuracy. |
SciMON: Scientific Inspiration Machines Optimized for Novelty (2024.acl-long)
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| Challenge: | Existing literature-based hypothesis generation models focus on binary link prediction, limiting expressivity of hypotheses. |
| Approach: | They propose a framework that uses literature-based hypothesis generation as input . they use literature-derived literature as background and output natural language ideas . |
| Outcome: | The proposed model improves the ability of language models to generate new scientific directions grounded in literature. |
Knowledge-Enriched Natural Language Generation (2021.emnlp-tutorials)
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| Challenge: | Knowledge-enriched text generation poses unique challenges in modeling and learning . a roadmap will outline the state-of-the-art methods to tackle these challenges . |
| Approach: | They propose a roadmap to tackle the challenges of knowledge-enriched text generation . they will dive deep into various technical components to illustrate how to represent knowledge . |
| Outcome: | This tutorial outlines the state-of-the-art methods to tackle the problem . it aims to show how to represent knowledge, feed knowledge into a generation model, evaluate results . |
Assessing and Verifying Task Utility in LLM-Powered Applications (2024.emnlp-main)
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Negar Arabzadeh, Siqing Huo, Nikhil Mehta, Qingyun Wu, Chi Wang, Ahmed Awadallah, Charles Clarke, Julia Kiseleva
| Challenge: | Rapid development of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents, assisting humans in their daily tasks. |
| Approach: | They propose a framework to propose criteria tailored to the unique purpose of any given application and propose corresponding criteria for the application. |
| Outcome: | The proposed framework provides a comprehensive assessment of the effectiveness and robustness of two open source datasets including Math Problem solving and ALFWorld House-hold related tasks. |
Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play (2024.emnlp-demo)
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Sha Li, Revanth Gangi Reddy, Khanh Nguyen, Qingyun Wang, Yi Fung, Chi Han, Jiawei Han, Kartik Natarajan, Clare Voss, Heng Ji
| Challenge: | Complex news events require swift responses from government and society, authors say . relying on historical events to project the future is insufficient, they say - a simulator for complex news events is needed . |
| Approach: | They propose a controllable complex news event simulator guided by event schema and user-provided assumptions . they incorporate a geo-diverse commonsense and cultural norm-aware knowledge enhancement component . |
| Outcome: | The proposed simulator achieves higher coherence and appropriateness than existing models. |
MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness (2025.emnlp-main)
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| Challenge: | Large language models produce non-existing facts when faced with questions outside their parametric knowledge, which undermines their reliability. |
| Approach: | They propose a method that separates the learning of answer prediction and confidence estimation during fine-tuning on instruction data. |
| Outcome: | Experiments on multiple models and different model sizes show that the proposed method outperforms baselines by up to 25% in average precision. |
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)
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Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych
| Challenge: | a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications . |
| Approach: | a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19. |
| Outcome: | a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing . |
Towards Objective Fine-tuning: How LLMs’ Prior Knowledge Causes Potential Poor Calibration? (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have enabled powerful domain-specific applications through supervised fine-tuning. |
| Approach: | They propose a cognition-aware framework that applies targeted learning strategies according to the model’s prior knowledge to improve calibration. |
| Outcome: | The proposed framework significantly improves calibration while maintaining performance, achieving an average 57% reduction in ECE compared to standard fine-tuning in Llama3-8B. |
OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agents (2026.acl-long)
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Bowen Yang, Kaiming Jin, Zhenyu Wu, Zhaoyang Liu, Qiushi Sun, Zehao Li, JingJing Xie, Zhoumianze Liu, Fangzhi Xu, Kanzhi Cheng, Yian Wang, Qingyun Li, Yu Qiao, Zun Wang, Zichen Ding
| Challenge: | Vision-Language Models (VLMs) lack visual-aware tutorial retrieval and historical visual context curation and pruning. |
| Approach: | They propose a framework that integrates an orchestrator and a Reflection-Memory Agent for robust automation. |
| Outcome: | Experimental results show that OS-Symphony delivers substantial performance gains across model scales. |
Paper Abstract Writing through Editing Mechanism (P18-2)
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| Challenge: | a paper abstract writing system can automatically generate an abstract from a title . a typical recurrent neural network (RNN) based approach easily loses focus. |
| Approach: | They propose a paper abstract writing system that automatically generates an abstract from a title. |
| Outcome: | The proposed system passes Turing tests by junior domain experts and non-experts at a rate up to 80%. |
Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction (2024.findings-eacl)
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| Challenge: | Existing frameworks for fine-grained few-shot entity extraction are difficult to implement in the chemical domain due to the information overload of scientific papers. |
| Approach: | They propose a sequence-to-sequence based few-shot entity extraction approach . it uses a seq2seq entity extractor and a self-validation module to reconstruct original input sentence . |
| Outcome: | The proposed framework achieves 8.26% and 6.84% performance gains on two datasets. |
TeamFusion: Supporting Open-ended Teamwork with Multi-Agent Systems (2026.acl-long)
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| Challenge: | Many group decisions are open-ended, and aggregation approaches suppress minority perspectives . team members must surface hidden assumptions, discuss disagreements, negotiate acceptable trade-offs . |
| Approach: | They propose a multi-agent system that instantiates a proxy agent for each team member . they also conduct a structured discussion to elicit agreements and disagreements . |
| Outcome: | The proposed system outperforms direct aggregation on two teamwork tasks . it can judge how well individual views are represented in team decisions and consensually good deliverables . |
Self-Correction is More than Refinement: A Learning Framework for Visual and Language Reasoning Tasks (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown remarkable abilities, but they invariably generate flawed responses. |
| Approach: | They propose a self-correction approach that instructs VLMs to refine their outputs by allowing them to learn from their self-generated self-reference data without external feedback. |
| Outcome: | The proposed approach enables VLMs to learn from their self-generated self-correction data without relying on external feedback, facilitating self-improvement. |
Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation (2026.findings-acl)
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| Challenge: | Multi-agent systems (MAS) are increasingly used for open-ended idea generation . when and why collective interaction expands the solution space remains unclear . |
| Approach: | They propose to study diversity in multi-agent systems across three bottom-up levels: model intelligence, agent cognition, and system dynamics. |
| Outcome: | The proposed model yields diminishing diversity despite higher quality . the proposed model fails to expand diversity and causes it to collapse . |
CiteGuard: Faithful Citation Attribution for LLMs via Retrieval-Augmented Validation (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have emerged as powerful assistants for scientific writing, but reliability of LLM alone is in doubt. |
| Approach: | They propose a retrieval-aware agent framework to provide more faithful grounding for citation validation. |
| Outcome: | The proposed framework improves over the baseline and achieves 68.1% accuracy on the CiteME benchmark, approaching human performance. |
Divide, Optimize, Merge: Scalable Fine-Grained Generative Optimization for LLM Agents (2025.findings-emnlp)
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Jiale Liu, Yifan Zeng, Shaokun Zhang, Chi Zhang, Malte Højmark-Bertelsen, Marie Normann Gadeberg, Huazheng Wang, Qingyun Wu
| Challenge: | LLM-based generative optimization has shown remarkable potential in improving agentic systems, but the current approach of prompting with the trajectories on the whole training dataset becomes untenable as datasets grow. |
| Approach: | They propose a scalable framework that divides large optimization tasks into manageable subsets and performs targeted optimizations. |
| Outcome: | The proposed framework outperforms conventional approach by 1.6-8.6% while reducing average prompt token consumption by 56.3%. |
CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) are pre-trained on extensive multilingual corpora to acquire both language-specific cultural knowledge and general knowledge. |
| Approach: | They propose to use the **C**ross-Lingual Self-**Aligning ability of **L**anguage **M**odels to align knowledge across languages. |
| Outcome: | The proposed model performs well in both zero-shot and retrieval-augmented settings. |
Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences (2024.naacl-long)
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| Challenge: | Existing models for named entity recognition fail in scientific domains such as biomedicine and chemistry. |
| Approach: | They propose a model to transfer knowledge from the biomedical domain to the target domain . they use pseudo labeling and contrastive learning to enhance discrimination . |
| Outcome: | The proposed model outperforms baseline models by up to 5% . the proposed model is based on a biomedical domain model and a chemical domain model . |
Multimedia Generative Script Learning for Task Planning (2023.findings-acl)
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| Challenge: | Goal-oriented generative script learning aims to generate subsequent steps to reach a specific goal . ability to capture historical states in visual modalities provides detailed information not covered by text . |
| Approach: | They propose a goal-oriented generative script learning task to generate subsequent steps by tracking historical states in both text and vision modalities. |
| Outcome: | The proposed task outperforms baselines in three aspects of the current task. |
PaperRobot: Incremental Draft Generation of Scientific Ideas (P19-1)
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| Challenge: | a paper robot can read existing papers and create new nodes or links in the knowledge graphs. |
| Approach: | They propose to automate the creation of new ideas by predicting links from the background KGs. |
| Outcome: | The proposed paper automates three tasks: read existing papers, create new ideas, predict links . the paper generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time. |
Towards a Human-Computer Collaborative Scientific Paper Lifecycle: A Pilot Study and Hands-On Tutorial (2024.lrec-tutorials)
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| Challenge: | a tutorial aims to provide an overview of the scientific paper lifecycle . large language models (LLMs) have increasingly played an important role in academic writing . |
| Approach: | They propose to provide an overview of the scientific paper lifecycle using large language models. |
| Outcome: | The tutorial will provide an overview of the scientific paper lifecycle, including scientific literature understanding, experiment development, manuscript draft writing, and finally draft evaluation. |
Language + Molecules (2024.eacl-tutorials)
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| Challenge: | In the last year, instruction-following language models have surged in popularity. |
| Approach: | This tutorial will provide an introduction to applying natural language-driven solutions to chemistry problems. |
| Outcome: | This tutorial will provide an introduction to this area of research. it requires no knowledge outside mainstream NLP, and it will enable participants to begin exploring relevant research. |
Stage-wise Fine-tuning for Graph-to-Text Generation (2021.acl-srw)
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| Challenge: | Graph-to-text generation has benefited from pre-trained language models (PLMs) but they fail to fully utilize the structure information of the input graph. |
| Approach: | They propose a structured graph-to-text model with a two-step fine-tuning mechanism which first fine-tracks model on Wikipedia before adapting to graph- to-text generation. |
| Outcome: | The proposed model improves the performance of the English WebNLG 2017 dataset by using tree-level embeddings to capture the inter-dependency structures of the input graph. |
SimpleDoc: Multi‐Modal Document Understanding with Dual‐Cue Page Retrieval and Iterative Refinement (2025.emnlp-main)
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| Challenge: | Document Visual Question Answering (DocVQA) is a task to answer questions based on documents containing text, tables, and images. |
| Approach: | They propose a lightweight retrieval framework that uses visual language models to embed and retrieve relevant pages as images and generate answers with VLMs that can accept an image as input. |
| Outcome: | The proposed framework outperforms baselines by 3.2% on average on 4 DocVQA datasets with much fewer pages retrieved. |