Papers by Mingyang Jiang
Butterfly Effects in Toolchains: A Comprehensive Analysis of Failed Parameter Filling in LLM Tool-Agent Systems (2025.findings-emnlp)
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| Challenge: | emergence of tool agent paradigm has broadened capability boundaries of the Large Language Model (LLM) but effectiveness of tool agents limited due to parameter failure during execution . |
| Approach: | They propose a parameter failure taxonomy to investigate parameter failure . they propose suggestions for standardizing tool return formats and improving error feedback mechanisms . |
| Outcome: | The proposed model is based on a tool agent invocation chain and a mainstream tool agent . it shows that parameter name hallucination failure stems from inherent limitations . |
When Evolution Strategy Meets Language Models Tuning (2025.coling-main)
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| Challenge: | Autoregressive language models with pretraining often display limited capability in effectively following instructions. |
| Approach: | They propose an on-policy approach to optimize models by harnessing the principle of biological evolution, namely survival of the fittest. |
| Outcome: | The proposed method can achieve superior performance in various tasks and comparable performance in the human alignment task. |
PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)
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Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, but they struggle to solve strictly constrained dialogue tasks. |
| Approach: | They construct a dataset that contains 12,705 high-quality Chinese dialogue instructions from 440 flowcharts containing 5,055 process nodes. |
| Outcome: | The proposed model outperforms GPT-4o models on backward transitions and outperformed GPT-42 models on the same dataset. |
Lion: Adversarial Distillation of Proprietary Large Language Models (2023.emnlp-main)
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| Challenge: | Existing methods for transferring knowledge from a proprietary large language model to an open-source LLM have been criticized for not incorporating any feedback to boost student model's proficiency iteratively. |
| Approach: | They propose a novel adversarial distillation framework that integrates feedback to identify challenging instructions where the student model’s performance falls short. |
| Outcome: | The proposed framework surpasses existing state-of-the-art LLMs by 55.4% in challenging zero-shot reasoning benchmarks and 16.7% on AGIEval. |
Mimicking the Familiar: Dynamic Command Generation for Information Theft Attacks in LLM Tool-Learning System (2025.acl-long)
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| Challenge: | Existing approaches to attack Large Language Model (LLM) tool-learning systems are black-box oriented and rely on static commands that cannot adapt flexibly to the changes in user queries and the invocation chain. |
| Approach: | They propose a dynamic attack comment generation approach for information theft attacks in LLM tool-learning systems that mimics the familiar by inferring the information utilized by upstream tools. |
| Outcome: | The proposed approach outperforms baselines with +13.2% ASRTheft and can be generalized to new tool-learning systems to expose their information leakage risks. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
Unsupervised Keyphrase Extraction by Learning Neural Keyphrase Set Function (2023.findings-acl)
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| Challenge: | Unsupervised keyphrase extraction is a task of extracting a keyphrase set that provides readers with highlevel information about the key ideas or important topics described in the document. |
| Approach: | They propose an unsupervised keyphrase extraction task that is a document-set matching problem instead of modeling the relevance between an individual phrase and the document. |
| Outcome: | The proposed model outperforms the state-of-the-art unsupervised keyphrase extraction baselines by a large margin. |
Are All Prompt Components Value-Neutral? Understanding the Heterogeneous Adversarial Robustness of Dissected Prompt in LLMs (2026.eacl-long)
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Yujia Zheng, Tianhao Li, Haotian Huang, Tianyu Zeng, Jingyu Lu, Chuangxin Chu, Yuekai Huang, Ziyou Jiang, Qian Xiong, Yuyao Ge, Mingyang Li
| Challenge: | Existing studies treat prompts as flat text, overlooking their internal structure, and different components within a prompt contribute unequally to robustness. |
| Approach: | They propose a framework that decomposes prompts into functional components and a method that selectively modifies components to expose component-wise vulnerabilities. |
| Outcome: | The proposed framework exposes component-wise vulnerabilities while ensuring linguistic plausibility through perplexity-based filtering. |
One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems (2025.findings-emnlp)
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Zhiyuan Chang, Mingyang Li, Xiaojun Jia, Junjie Wang, Yuekai Huang, Ziyou Jiang, Yang Liu, Qing Wang
| Challenge: | Existing knowledge poisoning attacks against RAG systems require multiple poisoned documents or can only function effectively on simplistic queries. |
| Approach: | They propose a more realistic knowledge poisoning attack that poisons only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements. |
| Outcome: | The proposed attack achieves success by poisoning only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements. |
All Changes May Have Invariant Principles: Improving Ever-Shifting Harmful Meme Detection via Design Concept Reproduction (2026.acl-long)
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Ziyou Jiang, Mingyang Li, Junjie Wang, Yuekai Huang, Jie Huang, Zhiyuan Chang, Zhaoyang Li, Qing Wang
| Challenge: | Existing methods for harmful meme detection only learn the combination of harmful elements and lack understanding of these implicit expressions. |
| Approach: | They propose a method that detects harmful memes by replicating the design concept of malicious users. |
| Outcome: | The proposed method achieves the highest accuracy with 81.1% and has slight accuracy decreases when generalized to type-shifting and temporal-evolving memes. |
Know Thy Enemy: Securing LLMs Against Prompt Injection via Diverse Data Synthesis and Instruction-Level Chain-of-Thought Learning (2026.findings-acl)
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Zhiyuan Chang, Mingyang Li, Yuekai Huang, Ziyou Jiang, Xiaojun Jia, Qian Xiong, Junjie Wang, Zhaoyang Li, Qing Wang
| Challenge: | Large language model (LLM)-integrated applications face security vulnerabilities from prompt injection (PI) attacks. |
| Approach: | They propose a model enhancement method that synthesizes diverse training data and employs instruction-level chain-of-thought fine-tuning to enable LLMs to effectively identify and reject malicious instructions regardless of their source or position in the context. |
| Outcome: | The proposed method outperforms baselines in three critical dimensions while maintaining utility performance without degradation. |
SAGE: Synergistic Adaptive Gating of Experts for Hateful Video Detection (2026.acl-long)
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| Challenge: | Existing methods for hateful video detection rely on multimodal feature fusion . existing methods rely only on blind feature mixing, which leads to feature dilution . |
| Approach: | They propose a framework that shifts from blind feature mixing to decision-level arbitration . it instantiates disentangled experts to rigorously preserve modality-specific semantics . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on HateMM and MultiHateClip benchmarks. |