Papers by Guoshuai Zhao
Duplicate-Aware Controlled Code Generation: Enhancing Copyright Protection with Targeted Reordering Beam Search in LLMs (2026.findings-acl)
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| Challenge: | Experimental results demonstrate that TRBS effectively reduces verbatim repetition while maintaining functional adequacy. |
| Approach: | They propose a plug-and-play decoding method that dynamically reorders beam candidates to reduce direct copying. |
| Outcome: | The proposed method reduces verbatim repetition while maintaining functional adequacy on a multi-language code generation benchmark. |
AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models (2026.findings-acl)
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Yimin Deng, Yejing Wang, Zhenxi Lin, Zichuan Fu, Guoshuai Zhao, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Xian Wu, Li Zhu, Xueming Qian
| Challenge: | Existing methods for temporal reasoning are limited and apply a fixed pipeline to all questions. |
| Approach: | They propose an adaptive temporal reasoning method that dynamically executes reasoning steps based on context and task requirements. |
| Outcome: | Experiments on two temporal QA benchmarks show the proposed method works. |
Pseudo-Label Enhanced Prototypical Contrastive Learning for Uniformed Intent Discovery (2024.findings-emnlp)
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| Challenge: | Existing methods focus on transferring in-domain (IND) prior knowledge to out-of-domain data through pre-training and clustering. |
| Approach: | They propose a Pseudo-Label enhanced Prototypical Contrastive Learning model for uniformed intent discovery that integrates supervised and pseudo signals from IND and OOD data. |
| Outcome: | The proposed method has been proven effective in two different settings of discovering new intents. |
A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs (2025.findings-acl)
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Yimin Deng, Yuxia Wu, Yejing Wang, Guoshuai Zhao, Li Zhu, Qidong Liu, Derong Xu, Zichuan Fu, Xian Wu, Yefeng Zheng, Xiangyu Zhao, Xueming Qian
| Challenge: | Existing methods focus on graph structure learning or semantic reasoning, lacking the capability to capture the inherent differences between historical and non-historical events. |
| Approach: | They propose a temporal knowledge graph reasoning framework that integrates both structural and semantic information to guide the reasoning process for different events. |
| Outcome: | The proposed framework integrates structural and semantic information to predict future events . it can provide evidence for many downstream tasks, including situation analysis and political decision making . |
Learning to Paraphrase for Alignment with LLM Preference (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) exhibit the issue of paraphrase divergence, which means that when a question is phrased in a slightly different but semantically similar way, LLM may output a wrong response . retraining faces challenges in meeting the computational costs and privacy security demands of LLMs. |
| Approach: | They propose a black-box method that enhances model performance by paraphrasing questions in expressions preferred by the model. |
| Outcome: | The proposed method improves performance by paraphrasing questions in expressions preferred by the model. |
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. |
MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning (2026.findings-acl)
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Yimin Deng, Zhenxi Lin, Yejing Wang, Guoshuai Zhao, Pengyue Jia, Zichuan Fu, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Li Zhu, Xian Wu, Xueming Qian
| Challenge: | Existing approaches focus on diagnostic reasoning based on internal model knowledge or static knowledge bases. |
| Approach: | They propose a two-stage diagnostic reasoning framework that integrates multi-perspective evidence to generate a diagnostic prediction. |
| Outcome: | The proposed method generates suspected diagnoses and reasoning traces from web search, SOAP-formatted case, and clinical case database. |
SEARCH-R: Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator for Multi-hop Question Answering (2026.findings-acl)
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FU Yuqing, Yimin Deng, Wanyu Wang, Yuhao Wang, Yejing Wang, Hongshi Liu, Yiqi Wang, Xiao Han, Maolin Wang, Guoshuai Zhao, Yi Chang, Xiangyu Zhao
| Challenge: | Existing approaches to multi-hop question answering lack effective control over reasoning paths, leading to astray results. |
| Approach: | They propose a framework for multi-hop question answering that trains an end-to-end reasoning path navigator to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model. |
| Outcome: | The proposed framework trains an end-to-end reasoning path navigator . it is able to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model . |