Papers by Baoyu Jing
Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-ray Reports (P19-1)
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| Challenge: | Existing studies do not consider the complex structure information between and within report sections. |
| Approach: | They propose a framework which exploits the structure information between and within report sections for generating CXR imaging reports. |
| Outcome: | The proposed framework achieves state-of-the-art performance on two CXR report datasets. |
Reasoning-Enhanced Domain-Adaptive Pretraining of Multimodal Large Language Models for Short Video Content Governance (2025.emnlp-industry)
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Zixuan Wang, Yu Sun, Hongwei Wang, Baoyu Jing, Xiang Shen, Xin Dong, Zhuolin Hao, Hongyu Xiong, Yang Song
| Challenge: | Existing approaches to identifying inappropriate content require extensive human-labeled data and lack cross-issue generalization. |
| Approach: | They propose a reasoning-enhanced multimodal large language model (MLLM) pretraining paradigm for unified inappropriate content detection. |
| Outcome: | The proposed model improves the MLLM's performance in both zero-shot and supervised fine-tuning settings and shows strong generalization capabilities to emergent, previously unseen issues. |
Not All Voices Are Rewarded Equally: Probing and Repairing Reward Models across Human Diversity (2025.findings-emnlp)
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Zihao Li, Feihao Fang, Xitong Zhang, Jiaru Zou, Zhining Liu, Wei Xiong, Ziwei Wu, Baoyu Jing, Jingrui He
| Challenge: | Using real-world datasets, we conduct the most comprehensive study to date, auditing various state-of-the-art reward models across nine sensitive attributes, including age, gender, ethnicity, etc. |
| Approach: | They propose a method to mitigate group disparities in reward modeling by using real-world data. |
| Outcome: | The proposed method is based on a population-based dataset with nine demographic attributes, including gender, ethnicity, age, gender, and ethnicity. |
On the Automatic Generation of Medical Imaging Reports (P18-1)
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| Challenge: | a complete medical imaging report contains multiple heterogeneous forms of information, including findings and tags . abnormal regions in medical images are difficult to identify and the reports are typically long, containing multiple sentences. |
| Approach: | They propose a multi-task learning framework which predicts tags and generates paragraphs for abnormal regions in medical images. |
| Outcome: | The proposed framework can generate long paragraphs on two publicly available datasets. |
Multiplex Graph Neural Network for Extractive Text Summarization (2021.emnlp-main)
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| Challenge: | Existing methods for extractive text summarization do not consider multiple types of inter-sentential relationships, nor model intra-sententential relationships. |
| Approach: | They propose a novel method to combine different types of relationships among sentences and words to model sentence embedding. |
| Outcome: | The proposed model is compared with existing methods on CNN/DailyMail benchmark dataset to demonstrate its effectiveness. |
Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision? (2025.acl-long)
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| Challenge: | Graph Neural Networks (GNNs) with CLIP pipeline are difficult because of the scarcity of labeled data and text supervision, different levels of downstream tasks, and conceptual gaps between domains. |
| Approach: | They propose a multi-modal prompt learning paradigm to adapt pre-trained GNNs to downstream tasks with weak text supervision. |
| Outcome: | The proposed model can generalize graphs to unseen classes with weak text supervision. |
RADAR: Risk-Aware Distilled Adaptive Routing for Efficient Short-Form Video Platform Ecosystem Governance (2026.acl-industry)
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| Challenge: | Existing solutions to address inefficiency in large-scale integrity enforcement on short-form video platforms require multiple specialized vertical modules . |
| Approach: | They propose a lightweight risk-aware routing framework that selectively releases low-risk content while dispatching high-risk instances to appropriate vertical modules. |
| Outcome: | The proposed framework selectively releases low-risk content while dispatching high-risk instances to appropriate vertical modules. |