Papers by Junran Wu
Rumor Detection on Social Media with Temporal Propagation Structure Optimization (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for detecting rumors on social media neglect the temporal aspect of rumor propagation. |
| Approach: | They propose a method that incorporates temporal information by building a weighted propagation tree and a coding tree. |
| Outcome: | The proposed approach preserves essential structure of rumor propagation while reducing noise. |
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing self-supervised methods in natural language processing rely on augmentation rules to generate contrastive samples. |
| Approach: | They propose a hierarchy-aware information lossless contrastive learning scheme that uses syntactic information reserved in the input sample and fused during the learning process. |
| Outcome: | The proposed learning scheme is superior to existing methods in hierarchical text classification . the proposed learning system is based on a structure encoder and a text encoder . |
CogGen: A Cognitively Inspired Recursive Framework for Deep Research Report Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to deep research report generation rely on rigid predefined linear workflows, which cause error accumulation and limit in-depth multimodal fusion and report quality. |
| Approach: | They propose a Cognitively inspired recursive framework for deep research report Generation that simulates cognitive writing and abstract visual representation (AVR) they also propose CLEF, a cognitive load evaluation framework, and a benchmark from our world in data. |
| Outcome: | The proposed framework achieves state-of-the-art among open-source systems, surpassing Gemini Deep Research. |
GR1: Reinforcement-Enhanced LLM for Geoscience Reasoning (2026.findings-acl)
Copied to clipboard
Yule Xie, Jiaxin Ding, Cheng Deng, Shiqing Gao, Junran Zhang, Sibo Zhang, Zeyuan Wang, Ke Wu, Xin Ding, Luoyi Fu, Meng Jin, Xinbing Wang
| Challenge: | Recent advances in large language models have demonstrated RL's substantial capacity to enhance multi-step reasoning beyond what supervised instruction tuning achieves. |
| Approach: | They propose a framework that converts multimodal questions into descriptive text . they propose RL-enhanced geoscience reasoning that can be fine-tuned to a text-only level . |
| Outcome: | The proposed framework improves accuracy and accuracy on multimodal questions while preserving answerability and difficulty. |
Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network (2022.coling-1)
Copied to clipboard
| Challenge: | Text classification is a primary task in natural language processing (NLP). |
| Approach: | They propose a graph neural network (HINT) that makes full use of hierarchical information contained in the text for the task of text classification. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on popular benchmarks while having a simple structure and few parameters. |
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models (2024.findings-acl)
Copied to clipboard
Noah Wang, Z.y. Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Jian Yang, Man Zhang, Zhaoxiang Zhang, Wanli Ouyang, Ke Xu, Wenhao Huang, Jie Fu, Junran Peng
| Challenge: | Large Language Models (LLMs) have paved the way for complex tasks such as role-playing. |
| Approach: | They propose a framework to benchmark, elicit, and enhance role-playing abilities in Large Language Models. |
| Outcome: | The proposed framework improves role-playing abilities with 168,093 samples. |
HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing dual-encoder methods in HTC achieve weak performance gains with huge memory overheads and their structure encoders heavily rely on domain knowledge. |
| Approach: | They propose a hierarchy-aware tree isomorphism network to enhance the text representations with only syntactic information of the label hierarchy. |
| Outcome: | The proposed model could boost the performance of hierarchical text classification without prior statistics or label semantics without prior data. |