Papers by Zhixin Chen
Multi-level Association Refinement Network for Dialogue Aspect-based Sentiment Quadruple Analysis (2025.acl-long)
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| Challenge: | Existing methods for identifying quadruples rely on predefined dialogue structure and word semantics to achieve accurate and comprehensive sentiment associations between utterances and words. |
| Approach: | They propose a multi-level association refinement network to achieve more accurate sentiment associations between utterances and words. |
| Outcome: | The proposed framework achieves state-of-the-art performance under low-resource conditions. |
UBench: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions (2025.findings-acl)
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Xunzhi Wang, Zhuowei Zhang, Gaonan Chen, Qiongyu Li, Bitong Luo, Zhixin Han, Haotian Wang, Zhiyu Li, Hang Gao, Mengting Hu
| Challenge: | Existing methods for benchmarking the uncertainty of large language models face challenges . existing methods require internal model access, additional training, or high computational costs . |
| Approach: | They propose a new benchmark for evaluating the uncertainty of large language models based on confidence intervals . UBench encompasses 11,978 multiple choice questions spanning knowledge, language, understanding, and reasoning capabilities. |
| Outcome: | The proposed method outperforms existing methods for benchmarking the uncertainty of large language models. |
Reasoning in the Dark: Interleaved Vision-Text Reasoning in Latent Space (2026.findings-acl)
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| Challenge: | Existing multimodal reasoning methods depend on explicit reasoning steps that require labor-intensive vision-text annotations and inherently introduce significant inference latency. |
| Approach: | They propose a method that integrates visual and visual information into the reasoning process to improve the performance of multimodal LLMs. |
| Outcome: | The proposed method achieves an average performance increase of 5.45% while achieving a speed increase of over 5 times compared to existing methods. |
Multimodal Pragmatic Jailbreak on Text-to-image Models (2025.acl-long)
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Tong Liu, Zhixin Lai, Jiawen Wang, Gengyuan Zhang, Shuo Chen, Philip Torr, Vera Demberg, Volker Tresp, Jindong Gu
| Challenge: | Existing jailbreaks for diffusion-based text-to-image models generate unsafe content . experimental results show that all tested models suffer from unsafe generation . |
| Approach: | They propose a jailbreak that triggers diffusion-based text-to-image models to generate the image with visual text, resulting in unsafe content. |
| Outcome: | The proposed model generates image with visual text, but the model is unsafe under such jailbreak. |
Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation (2025.acl-industry)
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Zixuan Wang, Jinghao Shi, Hanzhong Liang, Xiang Shen, Vera Wen, Zhiqian Chen, Yifan Wu, Zhixin Zhang, Hongyu Xiong
| Challenge: | Effective content moderation is essential for video platforms to safeguard user experience and uphold community standards. |
| Approach: | They propose a method to transform a generative MLLM into a multimodal classifier using minimal discriminative training data. |
| Outcome: | The proposed method improves F1 score by 66.50% over traditional classifiers while requiring only 2% of the fine-tuning data. |
LawBench: Benchmarking Legal Knowledge of Large Language Models (2024.emnlp-main)
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Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, Jidong Ge, Vincent Ng
| Challenge: | LegalBench evaluated 20 LLMs in 162 legal tasks in 20 countries and jurisdictions. |
| Approach: | They present a comprehensive evaluation of 21 popular Large Language Models and the first comparative analysis of the empirical results. |
| Outcome: | The proposed benchmarks are based on the Bloom’s cognitive taxonomy and are compared to 21 popular LLMs. |