Papers by Zhongyu Jiang
Bayesian Optimization for Controlled Image Editing via LLMs (2025.findings-acl)
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Chengkun Cai, Haoliang Liu, Xu Zhao, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, John Lee, Jenq-Neng Hwang, Lei Li
| Challenge: | achieving precise control over generated content and maintaining semantic consistency remain significant limitations, particularly concerning grounding techniques and the necessity for model fine-tuning. |
| Approach: | They propose an off-the-shelf approach that integrates Large Language Models with Bayesian Optimization to facilitate precise and user-friendly image editing. |
| Outcome: | The proposed approach outperforms existing methods in editing accuracy and semantic preservation, as validated using different LLMs including Claude3 and GPT-4. |
The Role of Deductive and Inductive Reasoning in Large Language Models (2025.acl-long)
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Chengkun Cai, Xu Zhao, Haoliang Liu, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, Jenq-Neng Hwang, Lei Li
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning tasks, yet their reliability in problem-solving remains debatable. |
| Approach: | They propose a framework that integrates both deductive and inductive reasoning approaches to enhance LLM reasoning by progressively adapting its reasoning pathways based on problem complexity. |
| Outcome: | The proposed framework achieves 70.3% accuracy on AIW, compared to 62.2% for Tree of Thought, while maintaining lower computational costs. |
Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text (2022.findings-acl)
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| Challenge: | Existing methods for logical reasoning of text focus on contextual semantics while struggling to explicitly model the logical inference process. |
| Approach: | They propose a logic-driven context extension framework and a data-driven augmentation algorithm that uses contrastive learning to better capture logical information. |
| Outcome: | The proposed framework outperforms existing methods on two benchmark datasets, ReClor and LogiQA. |
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)
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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan
| Challenge: | Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems. |
| Approach: | They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations. |
| Outcome: | The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods. |
Align Voting Behavior with Public Statements for Legislator Representation Learning (2021.acl-long)
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| Challenge: | Existing studies rely on roll call data to estimate political preference of legislators. |
| Approach: | They propose to integrate voting behavior and public statements on Twitter to jointly model legislators. |
| Outcome: | The proposed model improves on the task of roll call vote prediction . it also shows that the model captures nuances in statements . |
Leveraging Argumentation Knowledge Graph for Interactive Argument Pair Identification (2021.findings-acl)
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| Challenge: | Existing researches focus on sentence matching but the interaction of opinions requires reasoning of knowledge, which is beyond textual information. |
| Approach: | They propose to leverage external knowledge to enhance the identification of interactive argument pairs by analyzing the discussion thread of the target topic in an online forum. |
| Outcome: | The proposed model achieves state-of-the-art in the benchmark dataset. |
Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding (2025.findings-naacl)
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Xingjian Diao, Chunhui Zhang, Weiyi Wu, Zhongyu Ouyang, Peijun Qing, Ming Cheng, Soroush Vosoughi, Jiang Gui
| Challenge: | Multimodal foundation models have demonstrated significant success in tasks such as visual captioning, question answering, and image-text retrieval. |
| Approach: | They propose a specialized cognitive module, temporal working memory, which selectively retains task-relevant information across temporal dimensions. |
| Outcome: | The module retains task-relevant information across temporal dimensions, ensuring that critical details are preserved throughout the processing of video and audio content. |
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (2021.findings-acl)
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Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou
| Challenge: | Existing methods for injecting knowledge into pre-trained models are inconsistent and can flush out knowledge when multiple kinds of knowledge are injected. |
| Approach: | They propose a framework that retains the original parameters of pre-trained models fixed and supports the development of versatile knowledge-infused models. |
| Outcome: | The proposed framework retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused models. |
Music Audio-Visual Question Answering Requires Specialized Multimodal Designs (2026.findings-acl)
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Wenhao You, Xingjian Diao, Wenjun Huang, Chunhui Zhang, Keyi Kong, Weiyi Wu, Chiyu Ma, Zhongyu Ouyang, Tingxuan Wu, Ming Cheng, Soroush Vosoughi, Jiang Gui
| Challenge: | Music audio-visual question answering presents unique challenges with dense audio-visual content, intricate temporal dynamics, and the need for domain-specific knowledge. |
| Approach: | They analyze Music AVQA datasets and analyze their results to identify key design patterns . they propose concrete future directions for incorporating musical priors . |
| Outcome: | The proposed architectures are critical for success in Music AVQA, the authors argue . they suggest concrete future directions for incorporating musical priors . |
EmoRes: Toward Adaptive Psychological Support via User-Agnostic Benchmark and Topic-Mining Agent (2026.findings-acl)
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Zhengwei Zou, Xuanming Jiang, Baoyi An, Dingyu Nie, Zhengxing Fang, Qingyu Liu, Xueming Qian, Guoshuai Zhao, Zhongyu Yang
| Challenge: | Large language models generate fragmented and emotionally inconsistent dialogues lacking the therapeutic structure necessary for reliable assessment. |
| Approach: | They propose a framework that boosts psychological reasoning via a Topic-Mining Emotional Agent and a multi-perspective Self-Reflection Agent. |
| Outcome: | The proposed framework improves topic continuity, emotional coherence, and clinical interpretability over baselines and validated by ablation studies and human evaluations. |
Learning Musical Representations for Music Performance Question Answering (2024.findings-emnlp)
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| Challenge: | Existing methods for audio-visual learning fail to consider the distinctive characteristics of instruments and music. |
| Approach: | They propose to integrate multimodal interactions within the context of music data and annotate and release rhythmic and music sources in the current music datasets to enable the model to learn music characteristics. |
| Outcome: | The proposed model can learn music characteristics from the current music datasets and align its predictions with the temporal dimension. |
SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models (2025.emnlp-main)
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Xingjian Diao, Chunhui Zhang, Keyi Kong, Weiyi Wu, Chiyu Ma, Zhongyu Ouyang, Peijun Qing, Soroush Vosoughi, Jiang Gui
| Challenge: | Recent large language models have demonstrated impressive reasoning abilities, but their extension to the audio modality remains underexplored. |
| Approach: | They propose a rule-based reinforcement learning algorithm to equip LALMs with robust reasoning capabilities. |
| Outcome: | The proposed algorithm improves on the SoundMind benchmark. |
A Two Stage Adaptation Framework for Frame Detection via Prompt Learning (2022.coling-1)
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| Challenge: | Existing frameworks focus on a single scenario or issue, ignoring the special characteristics of frame detection that new events emerge continuously and policy agenda changes dynamically. |
| Approach: | They propose a framework to adapt to different contexts and frame typologies . they propose coding tasks that learn transferable encoders and verbalizers based on pivots and prompts - and generalization tasks that apply them to new issues and label sets. |
| Outcome: | The proposed framework shows superiority in both full-resource and low-resourced conditions. |
Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection (2020.emnlp-main)
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| Challenge: | Existing methods for fine-grained propaganda detection are not based on input-output data, but instead use declarative knowledge to detect propagandistic text fragments. |
| Approach: | They propose a method to inject declarative knowledge of fine-grained propaganda techniques into training data to get better representations of propagandistic texts. |
| Outcome: | The proposed method achieves superior performance on a large dataset for propaganda detection. |