Papers by Jue Wang
SkipBERT: Efficient Inference with Shallow Layer Skipping (2022.acl-long)
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| Challenge: | Pre-trained language models have significant demands in computation and inference time, limiting their use in resource-constrained or latencysensitive applications. |
| Approach: | They propose to encode text chunks into independent representations and skip computation of shallow layers to accelerate inference. |
| Outcome: | The proposed approach can reduce latency by 65% without sacrificing performance. |
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. |
Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders (2020.emnlp-main)
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| Challenge: | Named entity recognition and relation extraction are two important fundamental problems. |
| Approach: | They propose to design two separate encoders to capture two different types of information in the representation learning process. |
| Outcome: | The proposed methods show significant improvements on standard datasets. |
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. |
Pyramid: A Layered Model for Nested Named Entity Recognition (2020.acl-main)
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| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task. |
| Approach: | They propose a pyramid-like layered model for Nested Named Entity Recognition . token or text region embeddings are recursively inputted into L flat NER layers . |
| Outcome: | The proposed model achieves state-of-the-art F1 scores in nested NER on ACE-2004, ACE 2005, GENIA, and NNE. |
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. |
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. |