Papers by Yingjie Wu
CtrlNews: LLM-based Multi-Agent Controllable News Writing via Knowledge Gravitational Field (2025.findings-emnlp)
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| Challenge: | Current approaches to news writing rely on superficially retrieved information and oversimplified knowledge enumeration resulting in shallow, repetitive, and unordered outputs. |
| Approach: | They propose an LLM-based multi-agent controllable news writing framework called CtrlNews . they propose a fine-grained viewpoint control mechanism to regulate bias, emotion, and exaggeration attributes. |
| Outcome: | The proposed framework simulates expert questioning through automated role assignment and question generation followed by a three-layer hierarchical gravitational graph iteratively refined via expansion-reflection cycles. |
PCBERT: Parent and Child BERT for Chinese Few-shot NER (2022.coling-1)
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| Challenge: | Existing approaches to improve model performance on few-shot or zero-shot datasets are not effective for Chinese few- shot NER. |
| Approach: | They propose a prompt-based Parent and Child BERT for Chinese few-shot NER to train an annotating model on high-resource datasets and then discover more implicit labels on low-resourced datasets. |
| Outcome: | The proposed model can be used on Weibo and other Chinese NER datasets and it is shown to be effective in few-shot learning. |
THCM-CAL: Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction (2025.findings-emnlp)
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| Challenge: | Existing approaches to risk prediction from EHRs handle structured diagnostic codes and unstructured narrative notes separately. |
| Approach: | They propose a Temporal-Hierarchical Causal Model with Conformal Calibration . they construct a multimodal causal graph where nodes represent clinical entities from two modalities . |
| Outcome: | The proposed model infers three clinically grounded interactions from textual propositions and ICD codes mapped to textual descriptions. |
CmEAA: Cross-modal Enhancement and Alignment Adapter for Radiology Report Generation (2025.coling-main)
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| Challenge: | Existing methods for automatic radiology report generation suffer from data bias. |
| Approach: | They propose a method that connects a vision encoder with a frozen large language model by using a cross-modal enhancement and alignment adapter. |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on IU X-Ray and MIMIC-CXR datasets. |