Papers by Yufei Cui
EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing (2026.findings-acl)
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| Challenge: | Existing approaches to modifying large language models require continual updates to rectify outdated or erroneous knowledge. |
| Approach: | They propose a model editing strategy that mitigates catastrophic interference through sequential null-space alignment. |
| Outcome: | EvoEdit achieves better or comparable performance than prior state-of-the-art techniques with up to 3.53 speedup. |
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language Models (2024.findings-acl)
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Linhao Yu, Yongqi Leng, Yufei Huang, Shang Wu, Haixin Liu, Xinmeng Ji, Jiahui Zhao, Jinwang Song, Tingting Cui, Xiaoqing Cheng, Liutao Liutao, Deyi Xiong
| Challenge: | Recent years have witnessed remarkable progress achieved by large language models in both natural language understanding and generation. |
| Approach: | They propose a large benchmark CMoralEval for moral evaluation of Chinese LLMs . they use a Chinese TV program discussing Chinese moral norms and Chinese moral anomies based on various sources . |
| Outcome: | The proposed dataset is characterized by diversity and authenticity. |
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety (2024.acl-demos)
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Chuang Liu, Linhao Yu, Jiaxuan Li, Renren Jin, Yufei Huang, Ling Shi, Junhui Zhang, Xinmeng Ji, Tingting Cui, Liutao Liutao, Jinwang Song, Hongying Zan, Sun Li, Deyi Xiong
| Challenge: | a rapid development of Chinese large language models poses big challenges for efficient LLM evaluation. |
| Approach: | They propose an evaluation testbed that benchmarks Chinese LLMs across capability, alignment and safety. |
| Outcome: | The evaluation platform OpenEval benchmarks Chinese LLMs across capability, alignment and safety. |
FigEx: Aligned Extraction of Scientific Figures and Captions (2025.findings-emnlp)
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| Challenge: | FigEx is a vision-language model to extract aligned pairs of subfigures and subcaptions from scientific papers. |
| Approach: | They propose a vision-language model to extract aligned pairs of subfigures and subcaptions from scientific papers. |
| Outcome: | The proposed model improves subfigure detection APb over Grounding DINO by 0.023 and boosts caption separation BLEU over Llama-2-13B by 0.465. |
Thinking Long, but Short: Stable Sequential Test-Time Scaling for Large Reasoning Models (2026.findings-eacl)
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| Challenge: | Inducing models to think for longer can increase accuracy, but as the length of reasoning is further extended, it has also been shown to result in accuracy degradation and model instability. |
| Approach: | They propose a sequential test-time scaling method which induces models to think for longer, but which also generates an increasingly long output. |
| Outcome: | The proposed method improves model accuracy significantly over a wide range of induced thoughts, stabilizing the accuracy of sequential scaling, and eliminating the need for reasoning length fine-tuning. |
From General Reward to Targeted Reward: Improving Open-ended Long-context Generation Models (2025.emnlp-main)
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| Challenge: | Current research on long-form context in Large Language Models (LLMs) focuses on understanding of long-contexts, but the open-ended Long Text Generation (Open-LTG) remains underexplored. |
| Approach: | They propose a method that uses data synthesis and a reward signal to enhance model performance. |
| Outcome: | The proposed method outperforms GPT-4-Turbo and improves performance by 20% on the Open-LTG task. |
BOSCH: Black-Box Binary Optimization for Short-Context Attention-Head Selection in LLMs (2026.acl-long)
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| Challenge: | Existing hybridization schemes use sliding-window attention (SWA) to reduce KV cache usage and improve latency. |
| Approach: | They propose a training-free method that decomposes a large neighborhood search problem into three subproblems and a method that uses black-box binary optimization for short-context head selection. |
| Outcome: | Extensive experiments on 4 LLMs show that BOSCH outperforms layer-level heuristics and 6 strong static head-level methods with larger gains at higher SWA ratios. |
MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers (2026.acl-long)
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Linrui Ma, Chun Hei Lo, Xinyu Wang, Peng Lu, Xihao Yuan, Hanting Chen, Kai Han, Xinghao Chen, Chengjun Zhan, Hanlin xu, Yichun Yin, Lifeng Shang, Feng Wen, Boxing Chen, Yufei Cui
| Challenge: | Existing approaches to improve efficiency often enforce rigid structural constraints such as local attention windows. |
| Approach: | They propose a framework that augments sparse-attention mechanisms with dynamically integrated in-context information through an efficient retrieval system. |
| Outcome: | Empirical results show that MATCH significantly improves the performance of sparse-attention models on synthetic and real-world natural-language tasks. |