Papers by Jiaqi Mu
SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC (2024.findings-acl)
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| Challenge: | a growing number of cloud-based inference services are relying on SMPC to protect data privacy. |
| Approach: | They propose a framework for Privacy-Preserving Inference for Transformer models that eliminates exponential and maximum operations in PPI without sacrificing model performance. |
| Outcome: | The proposed framework outperforms MPCFormer in terms of performance and efficiency . it is 3.57 and 3.58 times faster than PUMA for BERTBASE and BERTLARGE . |
Preposition Sense Disambiguation and Representation (D18-1)
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| Challenge: | Prepositions are highly polysemous and their variegated senses encode significant semantic information. |
| Approach: | They match each preposition’s context and their interplay to the geometry of the word vectors to the left and right of the preposition. |
| Outcome: | The proposed algorithm is comparable to and better than state-of-the-art on two benchmark datasets. |
Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models (2026.acl-long)
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Runxuan Liu, Xianhao Ou, Xinyan Ma, Jiyuan Wang, Jiafeng Liang, Jiaqi Li, Tao He, Zheng Chu, Rongchuan Mu, Zekun Wang, Baoxin Wang, Dayong Wu, Ming Liu, Shijin Wang, Guoping Hu, Bing Qin
| Challenge: | Existing methods for long chain-of-thought (LCoT) are coarse-grained, reward hacking, and poor generalization. |
| Approach: | They propose a Long Chain-of-Thought (LCoT) model that integrates reinforcement learning with verifiable rewards with a process-aware verification approach. |
| Outcome: | The proposed model improves reasoning and code generation tasks while reducing the cost of training and performance bottlenecks. |
LMDX: Language Model-based Document Information Extraction and Localization (2024.findings-acl)
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Vincent Perot, Kai Kang, Florian Luisier, Guolong Su, Xiaoyu Sun, Ramya Sree Boppana, Zilong Wang, Zifeng Wang, Jiaqi Mu, Hao Zhang, Chen-Yu Lee, Nan Hua
| Challenge: | Large Language Models have revolutionized Natural Language Processing but their application in extracting information from visually rich documents has not been successful. |
| Approach: | They propose a language model-based document information extraction and localization methodology to reframe the document information extract task for a LLM. |
| Outcome: | The proposed method enables extraction of singular, repeated, and hierarchical entities with and without training data. |