Papers by Junjie Cao
Muse: Towards Reproducible Long-Form Song Generation with Fine-Grained Style Control (2026.findings-acl)
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Changhao Jiang, Jiahao Chen, Zhenghao Xiang, Zhixiong Yang, Hanchen Wang, Jiabao Zhuang, Xinmeng Che, Jiajun Sun, Hui Li, Yifei Cao, Shihan Dou, Ming Zhang, Junjie Ye, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Recent commercial systems such as Suno demonstrate strong capabilities in long-form song generation, but academic research remains non-reproducible due to the lack of publicly available training data. |
| Approach: | They propose a system for long-form song generation with fine-grained style conditioning that includes a licensed synthetic dataset and a song generation model, Muse. |
| Outcome: | The proposed system achieves competitive performance on phoneme error rate, text–music style similarity, and audio aesthetic quality while enabling controllable segment-level generation across different musical structures. |
ArchiDocGen: Multi-Agent Framework for Expository Document Generation in the Architectural Industry (2025.acl-industry)
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Junjie Jiang, Haodong Wu, Yongqi Zhang, Songyue Guo, Bingcen Liu, Caleb Chen Cao, Ruizhe Shao, Chao Guan, Peng Xu, Lei Chen
| Challenge: | drafting method statements is labor-intensive and time-consuming . traditional methods involve using static templates filled in manually by engineers . |
| Approach: | They propose a framework that automates method statement generation by using multi-agent collaboration. |
| Outcome: | The proposed framework achieves 4.38 ContentScore, excelling in specialization, completeness, organization, and clarity. |
Semantic Parsing for English as a Second Language (2020.acl-main)
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| Challenge: | Existing studies on domain adaptation in NLP focus on learning challenges at the syntax-semantics interface during second language acquisition. |
| Approach: | They propose to use English Resource Grammar and TLE to parse ESL data using a reranking model to evaluate the quality of the annotations. |
| Outcome: | The proposed model can obtain a very promising quality in comparison to human annotations. |
Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration (2024.findings-emnlp)
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Weikang Yuan, Junjie Cao, Zhuoren Jiang, Yangyang Kang, Jun Lin, Kaisong Song, Tianqianjin Lin, Pengwei Yan, Changlong Sun, Xiaozhong Liu
| Challenge: | Existing studies have found that when LLMs are given criminal facts and legal rules, then asked whether cases constitute a certain charge, they struggle to understand legal theories and perform basic legal reasoning tasks. |
| Approach: | They propose a task to assess LLMs' understanding of legal theories and reasoning capabilities by using a novel framework: Multi-Agent framework for improving complex legal reasoning capability. |
| Outcome: | The proposed framework improves LLMs' understanding of legal theories and reasoning abilities in real-world scenarios. |
LeCoDe: A Benchmark Dataset for Interactive Legal Consultation Dialogue Evaluation (2026.acl-long)
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Weikang Yuan, Kaisong Song, Zhuoren Jiang, Junjie Cao, Yujie Zhang, Jun Lin, Kun Kuang, Ji Zhang, Xiaozhong Liu
| Challenge: | Current systems for legal consultation are insufficient to handle the knowledge-intensive nature of real-world consultations. |
| Approach: | They propose a multi-turn benchmark dataset to evaluate LLMs in legal consultation settings. |
| Outcome: | The proposed framework assesses LLMs’ consultation capabilities in terms of (1) clarification capability and (2) professional advice quality. |
Entity Relation Extraction as Dependency Parsing in Visually Rich Documents (2021.emnlp-main)
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| Challenge: | Existing studies on key information extraction from visually rich documents focus on labeling the text within bounding boxes, while relations between words are unexplored. |
| Approach: | They propose to use a dependency parsing model to extract semantic entities from visually rich documents by combining entity labeling and relation extraction tasks. |
| Outcome: | The proposed model achieves 65.96% F1 score on the FUNSD dataset. |
Thinking Traps in Long Chain-of-Thought: A Measurable Study and Trap-Aware Adaptive Restart (2026.findings-acl)
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null Chenkang, Fan Yu, Junjie Nian, Sihan Zhao, Zhuoka Feng, Zijun Yao, Wang Heng, Yu Minshen, Yixin Cao
| Challenge: | Experiments show that extended generation does not guarantee correctness . a recurring pattern in Long-CoT failures is a problem for large reasoning models . |
| Approach: | They propose a test-time control framework that truncates the trajectory before the trap segment and adaptively restarts decoding. |
| Outcome: | Experiments show that TAAR improves reasoning performance without fine-tuning model parameters. |
Beyond Scaling: Measuring and Predicting the Upper Bound of Knowledge Retention in Language Model Pre-Training (2026.acl-long)
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Changhao Jiang, Ming Zhang, Yifei Cao, Junjie Ye, Xiaoran Fan, Shihan Dou, Zhiheng Xi, Jiajun Sun, Yi Dong, Yujiong Shen, Jingqi Tong, Baoyu Fan, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing methods to predict performance of large language models are lacking . authors propose a size-dependent mutual information predictor for closed-book question answering accuracy . |
| Approach: | They propose a size-dependent mutual information predictor that integrates knowledge frequency, knowledge specificity, and model size to forecast closed-book question answering accuracy. |
| Outcome: | The proposed method outperforms baseline models and achieves R2 > 0.7 in predicting QA accuracy without additional training. |