Papers by Jue Zhang
Draft
& Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding (2024.acl-long)
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| Challenge: | Existing methods for accelerating Large Language Models have been criticized for their inference costs and inefficient decoding. |
| Approach: | They propose a self-speculative decoding approach for accelerating Large Language Models without an auxiliary model. |
| Outcome: | The proposed method achieves a speedup of up to 1.99 with no additional neural network training and no extra memory footprint. |
Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents (2025.findings-emnlp)
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Shouju Wang, Fenglin Yu, Xirui Liu, Xiaoting Qin, Jue Zhang, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan
| Challenge: | Existing benchmarks for privacy performance of LLM agents are limited to static, simplified scenarios. |
| Approach: | They propose a model-agnostic, contextual integrity based mitigation approach that effectively reduces privacy leakage from 36.08% to 7.30% on DeepSeek-R1 and from 33.06% to 8.32% on GPT-4o. |
| Outcome: | The proposed approach reduces privacy leakage from 36.08% to 7.30% on DeepSeek-R1 and from 33.06% to 8.32% on GPT-4o while preserving task helpfulness. |
ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)
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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong
| Challenge: | Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs). |
| Approach: | They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models. |
| Outcome: | The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks. |
TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning (2025.findings-acl)
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Shivam Shandilya, Menglin Xia, Supriyo Ghosh, Huiqiang Jiang, Jue Zhang, Qianhui Wu, Victor Rühle, Saravan Rajmohan
| Challenge: | Existing prompt compression techniques rely on sub-optimal metrics such as information entropy or model it as a task-agnostic token classification problem that fails to capture task-specific information. |
| Approach: | They propose a task-aware prompt compression method that leverages existing Transformer encoders and a lightweight REINFORCE algorithm to ensure low latency requirements. |
| Outcome: | The proposed method improves task performance by 8% - 189% on three diverse and challenging tasks over state-of-the-art techniques while satisfying the same compression rate and latency requirements. |
DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains (2025.findings-emnlp)
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| Challenge: | Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs . current approaches encode graph context in textual form, which fails to exploit its potential . |
| Approach: | a new method is proposed to predict missing triples in knowledge graphs by leveraging existing triples and textual information. |
| Outcome: | The proposed model learns structural embeddings and logical rules within the KG and extracts a subgraph for each query guided by the learned rules. |
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation (2024.findings-emnlp)
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Jia Fu, Xiaoting Qin, Fangkai Yang, Lu Wang, Jue Zhang, Qingwei Lin, Yubo Chen, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
| Challenge: | Recent advances in Large Language Models have transformed ML/AI development . a reevaluation of AutoML principles for Retrieval-Augmented Generation (RAG) systems is needed. |
| Approach: | They propose a framework for hyper-parameter tuning and a hierarchical MAB method for efficient exploration of large search spaces. |
| Outcome: | The proposed framework outperforms baseline methods in more challenging optimization scenarios. |
EgoMemory: Memory-Augmented Personalized Retrieval for Long-Context Egocentric Video (2026.findings-acl)
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Yuanmin Tang, Jue Zhang, Xiaoting Qin, Jing Yu, Meikang Qiu, Gaopeng Gou, Gang Xiong, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Wu
| Challenge: | Existing egocentric video datasets do not support the personalization and long-context reasoning required for episodic memory retrieval. |
| Approach: | They propose a benchmark framework that uses MLLMs and reflective Chain-of-Thought to ground user queries in personal memory explicitly. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on three benchmarks . it can be used to generate detailed target video descriptions in long-context contexts based on user-specific object annotations enriched with user-specified object annotation data . |
CI-Work: Benchmarking Contextual Integrity in Enterprise LLM Agents (2026.acl-industry)
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Wenjie Fu, Xiaoting Qin, Jue Zhang, Qingwei Lin, Lukas Wutschitz, Robert Sim, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Enterprise LLM agents can dramatically improve workplace productivity, but their core capability, retrieving and using internal context to act on a user’s behalf, also creates new risks for sensitive information leakage. |
| Approach: | They propose a Contextual Integrity-grounded benchmark that simulates enterprise workflows across five information-flow directions and evaluates whether agents can convey *essential* content while withholding *sensitive* context in dense retrieval settings. |
| Outcome: | The proposed model demonstrates that privacy failures are prevalent in enterprise workflows and that higher task utility correlates with increased privacy violations. |
An Inversion Attack Against Obfuscated Embedding Matrix in Language Model Inference (2024.emnlp-main)
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| Challenge: | Recent studies have explored transforming user inputs to obfuscated embedded vectors, so that the data will not be eavesdropped by service providers. |
| Approach: | They propose to transform user inputs to obfuscated embedded vectors so that the data will not be eavesdropped by service providers. |
| Outcome: | The proposed inversion attack can recover user input 100% from the obfuscated vectors without a solid and deliberate security design and analysis . |
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression (2024.findings-acl)
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Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Menglin Xia, Xufang Luo, Jue Zhang, Qingwei Lin, Victor Rühle, Yuqing Yang, Chin-Yew Lin, H. Vicky Zhao, Lili Qiu, Dongmei Zhang
| Challenge: | Existing approaches to compress prompts only leverage unidirectional context, causing suboptimal results. |
| Approach: | They propose a task-agnostic prompt compression method that takes tokens from context . they use a Transformer encoder to capture all essential information needed for prompt compression . |
| Outcome: | The proposed method is 3x-6x faster than existing prompt compression methods and faster than baselines. |
AXIS: Efficient Human-Agent-Computer Interaction with API-First LLM-Based Agents (2025.acl-long)
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Junting Lu, Zhiyang Zhang, Fangkai Yang, Jue Zhang, Lu Wang, Chao Du, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | Multimodal large language models (MLLMs) have enabled LLM-based agents to directly interact with application user interfaces (UIs), enhancing agents’ performance in complex tasks. |
| Approach: | They propose a novel agent framework that prioritizes actions through application programming interfaces over UI actions and facilitates the creation and expansion of APIs through automated exploration of applications. |
| Outcome: | The proposed framework reduces task completion time by 65%-70% and cognitive workload by 38%-53% while maintaining accuracy of 97%-98% compared to humans. |
RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization (2026.acl-long)
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Yihong Dong, Xue Jiang, Yongding Tao, Huanyu Liu, Kechi Zhang, Lili Mou, Rongyu Cao, Yingwei MA, Jue Chen, Binhua Li, Zhi Jin, Fei Huang, Yongbin Li, Ge Li
| Challenge: | Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs). |
| Approach: | They propose a hybrid-policy optimization approach that synergizes internal exploitation with external data to achieve stronger reasoning capabilities. |
| Outcome: | The proposed approach achieves state-of-the-art performance on six math reasoning benchmarks and superior performance on out-of distribution reasoning tasks. |
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering (2023.emnlp-industry)
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Fangkai Yang, Pu Zhao, Zezhong Wang, Lu Wang, Bo Qiao, Jue Zhang, Mohit Garg, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Large Language Models (LLMs) have gained popularity but lack specific domain knowledge in domain-specific tasks. |
| Approach: | They propose a model interaction paradigm that empowers LLM to achieve better performance on domain-specific tasks where it is not proficient. |
| Outcome: | The proposed approach outperforms the commonly used LLM with retrieval methods in domain-specific tasks. |
From Reasoning to Answer: Empirical, Attention-Based and Mechanistic Insights into Distilled DeepSeek R1 Models (2025.emnlp-main)
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| Challenge: | Large Reasoning Models generate explicit reasoning traces alongside final answers . the extent to which these traces influence answer generation remains unclear . |
| Approach: | They conduct empirical evaluation of Large Reasoning Models that include explicit reasoning . they also show that answer tokens attend substantially to reasoning tokens . |
| Outcome: | The results show that including explicit reasoning improves answer quality across domains . they also show that answer tokens attend substantially to reasoning tokens - the authors . |