Papers by Boyan Han
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 . |
Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models (2026.acl-long)
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| Challenge: | Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation . fixed anchors can enforce constraints, but they often impose rigid spans, leading to truncated reasoning . |
| Approach: | They propose a method that dynamically estimates end-anchor positions to adjust generation length before iterative infilling. |
| Outcome: | The proposed method improves format compliance and answer accuracy on GSM8K and MATH. |
SQLAgent: Learning to Explore Before Generating as a Data Engineer (2026.findings-acl)
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| Challenge: | Existing approaches to large language models fail to generalize in complex, real-world settings due to database-specific nature of SQL reasoning. |
| Approach: | They propose a two-stage LLM-based framework that decouples knowledge acquisition from query generation. |
| Outcome: | The proposed framework significantly improves accuracy over baselines on large-scale benchmarks. |
Hard to Read, Easy to Jailbreak: How Visual Degradation Bypasses MLLM Safety Alignment (2026.findings-acl)
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| Challenge: | Recent advances in visual context compression enable MLLMs to process ultra-long contexts efficiently by rendering text into images. |
| Approach: | They propose a strategy that decouples visual transcription from safety auditing by enforcing a serialized pipeline to decouple visual transcription and safety assessment. |
| Outcome: | The proposed strategy decouples visual transcription from safety auditing to reduce the risk of jailbreaking. |
ROSE: An Intent-Centered Evaluation Metric for NL2SQL (2026.acl-long)
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| Challenge: | Existing evaluation metrics for evaluating the effectiveness of Natural Language to SQL (NL2SQL) solutions are becoming unreliable due to its sensitiveness to syntactic variation and inconsistent consistency with ground-truth SQL. |
| Approach: | They propose an intent-centered metric that focuses on whether the predicted SQL answers the question, rather than consistency with the ground-truth SQL. |
| Outcome: | The proposed metric outperforms the next-best metric by nearly 24% on the expert-aligned validation set **ROSE-VEC**. |