Papers by Haowei Wang
From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial Injection (2025.naacl-long)
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| Challenge: | Toolcalling has changed Large Language Model (LLM) applications by integrating external tools, but it also introduces new security vulnerabilities, particularly in the tool scheduling mechanisms of LLM, which have not been extensively studied. |
| Approach: | They propose a framework that exploits vulnerabilities in Large Language Models through adversarial tool injection to execute privacy theft, launch denial-of-service attacks, and manipulate business competition. |
| Outcome: | The proposed framework exploits vulnerabilities in LLM tool-calling systems through adversarial tool injection. |
OpenT2T: An Open-Source Toolkit for Table-to-Text Generation (2024.emnlp-demo)
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Haowei Zhang, Shengyun Si, Yilun Zhao, Lujing Xie, Zhijian Xu, Lyuhao Chen, Linyong Nan, Pengcheng Wang, Xiangru Tang, Arman Cohan
| Challenge: | Existing methods for table-to-text generation are limited and benchmarked on a limited number of datasets. |
| Approach: | They propose to use open-source tools to reproduce existing large language models for performance comparison and expedite the development of new models. |
| Outcome: | The proposed toolkit compares existing large language models on 9 table-to-text generation datasets and maintains a leaderboard to provide insights for future work. |
HyperIDP: Customizing Temporal Hypergraph Neural Networks for Multi-Scale Information Diffusion Prediction (2025.coling-main)
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| Challenge: | Existing studies on information diffusion prediction have focused on both macroscopic and microscopic scales. |
| Approach: | They propose a hypergraph-based model that manages both macroscopic and microscopic diffusion predictions. |
| Outcome: | The proposed model outperforms baseline models on both macroscopic and microscopic tasks. |
CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks (2026.acl-long)
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Lingyue Fu, Hao Guan, Bolun Zhang, Haowei Yuan, Yaoming Zhu, Lin Qiu, ZongYu Wang, Xuezhi Cao, Xunliang Cai, Weiwen Liu, Weinan Zhang, Yong Yu
| Challenge: | Existing large language models for software engineering rely on coarse-grained pass rates obscuring specific cognitive bottlenecks. |
| Approach: | They propose a repository-level benchmark that dissects coding capabilities through atomized tasks. |
| Outcome: | The proposed framework achieves a 78.55% validity yield, surpassing the 31.7% retention rate of SWE-bench-Verified. |