Papers by Zhengwei Tao
MEEL: Multi-Modal Event Evolution Learning (2024.findings-acl)
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| Challenge: | Existing models fail to grasp the principles governing event evolution in various scenarios. |
| Approach: | They propose a multi-modal event evolution learning approach to grasp event evolution . they propose an instruction encapsulation process that transforms evolving graphs into instruction-tuning data . |
| Outcome: | The proposed model grasps the event evolution mechanism yielding advanced MMER ability. |
Towards General Agentic Intelligence via Environment Scaling (2026.findings-acl)
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Runnan Fang, Shihao Cai, Baixuan Li, Jialong Wu, Guangyu Li, Wenbiao Yin, Xinyu Wang, Xiaobin Wang, Liangcai Su, Zhen Zhang, Shibin Wu, Zhengwei Tao, Yong Jiang, Pengjun Xie, Ningyu Zhang, Fei Huang, Wentao Zhang, Jingren Zhou
| Challenge: | Diverse real-world APIs require precise, robust function-calling intelligence, which needs agents to develop these capabilities through interaction in varied environments. |
| Approach: | They propose a framework that scales up environments to enable agentic intelligence . they use a two-phase agent fine-tuning strategy to first endow agents with basic agentic capabilities, then specializing them for domain-specific contexts. |
| Outcome: | Experiments on -bench, -Bench, and ACEBench show that the model significantly enhances the models’ function-calling capability. |
EVIT: Event-Oriented Instruction Tuning for Event Reasoning (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have made significant advances in event reasoning . however, smaller instruction-tuned models do not consistently demonstrate exceptional proficiency . |
| Approach: | They propose an event-oriented instruction tuning technique to train a large language model . they propose a structure named event quadruple which contains the structure and semantics of events . |
| Outcome: | The proposed model achieves competitive performances on event reasoning tasks. |
Benchmarking Long-Context Language Models on Long Code Understanding (2025.acl-long)
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Jia Li, Xuyuan Guo, Lei Li, Kechi Zhang, Ge Li, Jia Li, Zhengwei Tao, Fang Liu, Chongyang Tao, Yuqi Zhu, Zhi Jin
| Challenge: | Currently, long-context language models are limited by the lack of a rigorous evaluation framework for long code understanding. |
| Approach: | They propose to use a long code understanding benchmark LongCodeU to evaluate LCLMs' long code comprehension ability for practical applications. |
| Outcome: | The proposed benchmarks show that current LCLMs are limited in their long code understanding ability, particularly when the long code length is greater than 32K, falling far short of their claimed 128K to 1M context windows. |
Nested Browser-Use Learning for Agentic Information Seeking (2026.acl-long)
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Baixuan Li, Jialong Wu, Wenbiao Yin, Kuan Li, Zhongwang Zhang, Huifeng Yin, Zhengwei Tao, Liwen Zhang, Pengjun Xie, Jingren Zhou, Yong Jiang, Wentao Zhang, Zhiqiang Gao
| Challenge: | Existing information-seeking (IS) agents rely on the web for their information acquisition. |
| Approach: | They propose a browser-action framework that decouples interaction control from page exploration through a nested structure. |
| Outcome: | Empirical results show that NestBrowse offers clear benefits in practice. |
Revisit Self-Debugging with Self-Generated Tests for Code Generation (2025.acl-long)
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Xiancai Chen, Zhengwei Tao, Kechi Zhang, Changzhi Zhou, Xinyu Zhang, Wanli Gu, Yuanpeng He, Mengdi Zhang, Xunliang Cai, Haiyan Zhao, Zhi Jin
| Challenge: | Large language models (LLMs) have made significant advances in code generation, but they still face challenges when tackling complex programming tasks beyond their basic capabilities. |
| Approach: | They propose to integrate self-generated tests into the code generation process . they propose to use post-execution and in-exection self-debugging to mitigate test bias . |
| Outcome: | The proposed method improves the performance of large language models in code generation tasks by leveraging execution feedback from tests. |
AELC: Adaptive Entity Linking with LLM-Driven Contextualization (2025.findings-emnlp)
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| Challenge: | Entity linking (EL) focuses on associating ambiguous mentions in text with corresponding entities in a knowledge graph. |
| Approach: | Entity linking (EL) focuses on associating ambiguous mentions in text with corresponding entities in a knowledge graph. |
| Outcome: | Experiments on four public benchmark datasets show that AELC achieves state-of-the-art performance. |
Integrating Physician Diagnostic Logic into Large Language Models: Preference Learning from Process Feedback (2024.findings-acl)
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| Challenge: | Existing studies have shown that large language models can enhance response richness and coherence, but there is a pressing need to bolster the model’s capacity for diagnostic logic to ensure patient safety. |
| Approach: | They propose an approach termed preference learning from process feedback (PLPF) that integrates the doctor’s diagnostic logic into LLMs. |
| Outcome: | The proposed approach improves the diagnostic accuracy of the baseline model in medical conversations by 17.6%, surpassing the performance of traditional approaches. |
ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphs (2024.findings-acl)
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| Challenge: | Existing approaches to integrate large language models and knowledge graphs with LLMs often ignore the rich cognitive potential inherent in KGs. |
| Approach: | They propose an observation-driven agent framework that integrates KG reasoning abilities via global observation and integrates it into the action and reflection modules. |
| Outcome: | The proposed framework improves on several datasets and achieves 12.87% and 8.9% accuracy improvements. |
SEAG: Structure-Aware Event Causality Generation (2023.findings-acl)
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Zhengwei Tao, Zhi Jin, Xiaoying Bai, Haiyan Zhao, Chengfeng Dou, Yongqiang Zhao, Fang Wang, Chongyang Tao
| Challenge: | Current methods for extracting event causality are limited by the lack of cross-task dependencies and may cause error propagation. |
| Approach: | They propose an approach for Structure-Aware Event Causality Generation (SEAG) they generate the ECG structure using a pre-trained language model and perform structural discriminative training alongside auto-regressive generation. |
| Outcome: | The proposed method is effective in extracting event causality from text. |
UniEvent: Unified Generative Model with Multi-Dimensional Prefix for Zero-Shot Event-Relational Reasoning (2023.acl-long)
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| Challenge: | Reasoning about events and their relations is an indispensable ability to fulfill various event-centric or common-sense reasoning tasks. |
| Approach: | They propose a multi-task learning framework that organizes event relational reasoning tasks into a coordinate system with multiple axes, representing inter-event relations and reasoning formulations. |
| Outcome: | The proposed framework achieves state-of-the-art or competitive performance on zero-shot and supervised reasoning tasks. |
PlugMed: Improving Specificity in Patient-Centered Medical Dialogue Generation using In-Context Learning (2023.findings-emnlp)
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| Challenge: | In-context learning is a key task in health conversational assistants, but it is difficult to guarantee the specificity of the responses. |
| Approach: | They propose a plug-and-play medical dialogue system that provides a patient-centered medical interpretation service to users who are less knowledgeable about medical knowledge. |
| Outcome: | The proposed model improves the specificity of the patient-centered medical dialogues by providing them with real dialogues from similar patients as prompts. |