Papers by Xiaomeng Jin
LUME: LLM Unlearning with Multitask Evaluations (2025.findings-emnlp)
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Anil Ramakrishna, Yixin Wan, Xiaomeng Jin, Kai-Wei Chang, Zhiqi Bu, Bhanukiran Vinzamuri, Volkan Cevher, Mingyi Hong, Rahul Gupta
| Challenge: | Unlearning aims to remove copyrighted, sensitive, or private content from large language models without a full retraining. |
| Approach: | They propose a multi-task unlearning benchmark LUME that unlearns short novels, biographies and public biographie . |
| Outcome: | The proposed benchmark unlearns short novels, biographies and public biographie . it also releases fine-tuned models with 1B and 7B parameter sizes as targets . |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
SYNTHIA: Novel Concept Design with Affordance Composition (2025.acl-long)
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Hyeonjeong Ha, Xiaomeng Jin, Jeonghwan Kim, Jiateng Liu, Zhenhailong Wang, Khanh Duy Nguyen, Ansel Blume, Nanyun Peng, Kai-Wei Chang, Heng Ji
| Challenge: | Existing studies on concept design using text-to-image models have enabled rapid ideation of novel visual concepts. |
| Approach: | They propose a framework for generating novel, functionally coherent designs based on desired affordances by decomposing concepts into parts and affordance . they also develop a curriculum learning scheme that fine-tunes T2I models to progressively learn affordance composition while maintaining visual novelty. |
| Outcome: | The proposed framework outperforms state-of-the-art models for novelty and functional coherence in human evaluation. |
Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate (2025.naacl-long)
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Xiaomeng Jin, Zhiqi Bu, Bhanukiran Vinzamuri, Anil Ramakrishna, Kai-Wei Chang, Volkan Cevher, Mingyi Hong
| Challenge: | Existing methods to remove unwanted knowledge from large language models are formulated as minimizing memorization through the loss of the model. |
| Approach: | They propose a normalized gradient difference algorithm that optimizes a forgetting objective and an automatic learning rate scheduler that allows for better control over the trade-off between the objectives. |
| Outcome: | The proposed method improves on TOFU and MUSE datasets while exhibiting stable training. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
Event Schema Induction with Double Graph Autoencoders (2022.naacl-main)
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| Challenge: | Experimental results show that a new method for learning event schemas from historical events is effective. |
| Approach: | They propose a new event schema induction framework which captures global dependencies among nodes in event graphs. |
| Outcome: | Experimental results show that the proposed model can learn event schemas with global consistency. |
Adversarial Robustness for Large Language NER models using Disentanglement and Word Attributions (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) tasks are becoming more challenging due to the introduction of complex tagsets, which often leads to the failure of existing NER systems in accurately recognizing these entities. |
| Approach: | They propose a novel attack which relies on disentanglement and word attribution techniques to learn an embedding and identifying important words across both components. |
| Outcome: | The proposed approach improves the F1 score over the original LLM model by 8% and 18% on CoNLL-2003 and Ontonotes 5.0 datasets respectively. |
Schema-based Data Augmentation for Event Extraction (2024.lrec-main)
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| Challenge: | Existing data augmentation methods rely on language models to train event extraction models. |
| Approach: | They propose a schema-based data augmentation method that utilizes event schemas to guide the data generation process. |
| Outcome: | The proposed method produces high-quality generated data and significantly improves model performance. |
Dynamics of Cognitive Heterogeneity: Investigating Behavioral Biases in Multi-Stage Supply Chains with LLM-Based Simulation (2026.acl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) allow repeatable experiments in which individual characteristics can be precisely defined. |
| Approach: | They propose a scalable experimental paradigm using Large Language Models to simulate multi-stage supply chain dynamics. |
| Outcome: | The proposed model systematically replicates and validates the results of a behavioral simulation on agents in multi-stage supply chain dynamics. |