Papers by Zhibo Xu
Enhancing Model Privacy in Federated Learning with Random Masking and Quantization (2025.findings-emnlp)
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Zhibo Xu, Zhu JianHao, Jingwen Xu, Changze Lv, Zhenghua Wang, Zisu Huang, Xiaohua Wang, Muling Wu, Qi Qian, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | federated learning approaches are limited by the complexity of large language models and the need for specialized expertise to protect intellectual property. |
| Approach: | They propose a federated learning approach that leverages random masking to obscure a subnetwork of model parameters and applies quantization to the remaining parameters. |
| Outcome: | The proposed approach maintains strong model performance in federated learning settings and achieves enhanced protection of model parameters compared to baseline methods. |
Can Multi-agent Help Disambiguation in Multi-domain Translation? (2026.findings-acl)
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| Challenge: | Existing multi-agent systems have shown strong potential for machine translation (MT) but their performance in multidomain translation remains unsatisfactory due to cross-domain word ambiguity . |
| Approach: | They propose a multi-agent collaborative disambiguation framework for MDT that leverages the collaborative capabilities of LLMs for disambiguations. |
| Outcome: | The proposed framework improves translation performance across multiple domains and improves disambiguation accuracy. |
LLMs Meet Isolation Kernel: Lightweight, Learning-free Binary Embeddings for Fast Retrieval (2026.findings-acl)
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| Challenge: | Large language models (LLMs) embeddings are typically high-dimensional, leading to substantial storage and retrieval overhead. |
| Approach: | They propose a learning-free method that transforms an LLM embedding into a binary embeddable using Isolation Kernel (IKE). |
| Outcome: | The proposed method performs 16.7 faster retrieval and 16 lower memory usage than the original LLM embeddings while maintaining comparable accuracy. |
Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)
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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains. |
| Approach: | They propose several strategies for deploying RAG that balance performance and efficiency. |
| Outcome: | The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy. |
DMDTEval: An Evaluation and Analysis of LLMs on Disambiguation in Multi-domain Translation (2025.emnlp-main)
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| Challenge: | Currently, Large Language Models (LLMs) have achieved remarkable results in machine translation, but their performance in multidomain translation (MDT) is less satisfactory. |
| Approach: | They propose to evaluate the disambiguation ability of Large Language Models in multi-domain translation . they construct a translation test set with multi- domain ambiguous word annotation . |
| Outcome: | The proposed framework evaluates LLMs on disambiguation in multi-domain translation (DMDTEval) the results show that LLM's perform poorly in multidomain translation, highlighting ambiguity in translation. |
UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents (2026.acl-long)
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Yifan Ji, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Qian Zhang, Zhibo Yang, Junyang Lin, Yu Gu, Ge Yu, Maosong Sun
| Challenge: | Recent Large Multimodal Models (LMMs) have shown promising potential for performing end-to-end KIE directly from document images. |
| Approach: | They propose a benchmark to evaluate the performance of Large Multimodal Models (LMMs) using a constrained-category KIE track and an open-categorical KIE Track. |
| Outcome: | Experiments on 15 state-of-the-art LMMs show performance degradation under diverse schema definitions, long-tail key fields, and complex layouts, along with pronounced performance disparities across different document types and scenarios. |
SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models (2025.findings-emnlp)
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| Challenge: | Large Language Models struggle with multilingual reasoning tasks due to resource constraints . a training-free method improves performance on multilingual thinking tasks . |
| Approach: | They propose a training-free method that transforms language-specific semantic information into language-agnostic structured representations. |
| Outcome: | The proposed method outperforms strong baselines on multilingual reasoning tasks. |
ICL: Iterative Continual Learning for Multi-domain Neural Machine Translation (2024.findings-emnlp)
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| Challenge: | Existing studies have focused on learning domain knowledge from multiple domains, but task-specific parameters hinder mutual transfer of knowledge between new domains. |
| Approach: | They propose an iterative Continual learning framework for multi-domain neural machine translation that leverages previously acquired domain knowledge. |
| Outcome: | The proposed model outperforms baseline models on UM-Corpus and OPUS datasets. |
Mitigating Position Bias in Transformers via Layer-Specific Positional Embedding Scaling (2026.findings-acl)
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Zhenghua Wang, Yiran Ding, Changze Lv, Yixin Wu, Tianlong Li, Zhibo Xu, Muling Wu, Tianyuan Shi, Shizheng Li, Qi Qian, Xuanjing Huang, Xiaoqing Zheng
| Challenge: | Existing methods to address the "lost-in-the-middle" problem suffer from high latency or suboptimal hand-crafted scaling strategies. |
| Approach: | They propose a layer-specific positional embedding scaling method that assigns distinct scaling factors to each layer. |
| Outcome: | Experiments show that the proposed method mitigates positional attention bias and delivers consistent improvements across multiple long-context benchmarks. |