Papers by Xinyu Xing
EFUF: Efficient Fine-Grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models (2024.emnlp-main)
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| Challenge: | Existing methods to eliminate hallucinations require expensive human annotation . hallucination in multimodal large language models poses unique challenges for current research . |
| Approach: | They propose a fine-grained unlearning framework that performs gradient ascent to eliminate hallucinations without paired data. |
| Outcome: | The proposed method reduces hallucinations while preserving quality with modest computational overhead. |
RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios (2025.findings-emnlp)
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Fei Zhao, Chengqiang Lu, Yufan Shen, Qimeng Wang, Yicheng Qian, Haoxin Zhang, Yan Gao, null Yiwu, Yao Hu, Zhen Wu, Shangyu Xing, Xinyu Dai
| Challenge: | RealBench is the first Chinese multimodal multi-image dataset . the dataset contains 9393 samples and 69910 images . |
| Approach: | They propose to create a Chinese multimodal multi-image dataset using 21 models . they use closed-source models that support multi-inputs as well as open-source visual and video models a . |
| Outcome: | The first Chinese multimodal multi-image dataset contains 9393 samples and 69910 images. |
Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Processing (2022.emnlp-main)
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Abbas Ghaddar, Yimeng Wu, Sunyam Bagga, Ahmad Rashid, Khalil Bibi, Mehdi Rezagholizadeh, Chao Xing, Yasheng Wang, Xinyu Duan, Zhefeng Wang, Baoxing Huai, Xin Jiang, Qun Liu, Phillippe Langlais
| Challenge: | Existing pre-trained language models are not well-explored and are not reproducible in the literature. |
| Approach: | They propose to improve existing Arabic language pre-trained language models using a more methodical approach. |
| Outcome: | The proposed models outperform existing models on ALUE, a leaderboard-powered benchmark for Arabic NLU and NLG tasks. |
Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums (P19-1)
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| Challenge: | Existing work on teaching machines to ask questions focused on generating fixed answers. |
| Approach: | They propose a model to generate open-answered questions from real-world news for open discussion . they analyze how language use affects the number of answers . |
| Outcome: | The proposed model generates questions with higher quality than most text generation methods. |
Automatic Generation of Citation Texts in Scholarly Papers: A Pilot Study (2020.acl-main)
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| Challenge: | Existing studies on automatic generation of citation texts in scholarly papers have not investigated this problem. |
| Approach: | They propose to train an implicit citation extraction model based on BERT and a multi-source pointer-generator network with cross attention mechanism for citation text generation. |
| Outcome: | The proposed model can generate short texts to describe cited papers in scholarly papers with training data. |
Structure-Aware Pre-Training for Table-to-Text Generation (2021.findings-acl)
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| Challenge: | Pretraining techniques have achieved great success on table-to-text generation. |
| Approach: | They propose a pre-trained model that is trained with tables and their contexts to generate fluent text from table input. |
| Outcome: | The proposed model can understand the structured input table and generate fluent text. |