Papers by Sicheng Yu
Interventional Training for Out-Of-Distribution Natural Language Understanding (2022.emnlp-main)
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| Challenge: | Existing methods for NLU training use only known and single confounders, but in many NLU tasks the confounder can be unknown and multifactorial. |
| Approach: | They propose a method that performs multi-granular intervention with identified multifactorial confounders by using a bottom-up automatic intervention method. |
| Outcome: | The proposed method performs multi-granular intervention with identified multifactorial confounders on three NLU tasks, namely, natural language inference, fact verification and paraphrase identification. |
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| Challenge: | Existing approaches to modifying large language models require continual updates to rectify outdated or erroneous knowledge.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. |
Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models (2025.acl-long)
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JianXing Liao, Junyan Xu, Yatao Sun, Maowen Tang, Sicheng He, Jingxian Liao, Shui Yu, Yun Li, Xiaohong Guan
| Challenge: | Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency. |
| Approach: | They propose a language-guided framework that integrates large language models with computer-automated design to address these challenges. |
| Outcome: | The proposed framework outperforms traditional methods in accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts. |
NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset (2021.findings-emnlp)
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| Challenge: | Existing question answering datasets lack numerical reasoning and reasoning processes . current research on numerical reasoning focuses on simple calculations . |
| Approach: | They propose a conversational and bilingual question answering dataset with numerical reasoning with compound mathematical expressions. |
| Outcome: | The proposed model achieves 55.5 exact match scores while human performance is 89.7. |
Reverse Modeling in Large Language Models (2025.naacl-short)
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| Challenge: | Using pre-trained LLMs with reversed text inputs can improve their performance across multiple languages. |
| Approach: | They propose a way to determine whether LLMs can understand reversed text inputs by reversing entire paragraphs or documents at the token level. |
| Outcome: | The proposed model can be used to improve understanding across multiple languages. |
Towards Provably Secure Generative AI: Reliable Consensus Sampling (2026.findings-acl)
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Yu Cui, Hang Fu, Sicheng Pan, Zhuoyu Sun, Yifei Liu, Yuhong Nie, Bo Ran, Baohan Huang, Xufeng Zhang, Haibin Zhang, Cong Zuo, Licheng Wang
| Challenge: | Existing research on generative AI security is driven by mutually reinforcing attack and defense methodologies grounded in empirical experience. |
| Approach: | They propose a new algorithm that uses a random sampling algorithm to control risk. |
| Outcome: | The proposed algorithm improves robustness and utility while maintaining latency comparable to existing algorithms. |
COSY: COunterfactual SYntax for Cross-Lingual Understanding (2021.acl-long)
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| Challenge: | Pre-trained multilingual language models suffer from a large performance gap between source and target languages . e.g., multilingual-BERT models are widely used in cross-lingual tasks . |
| Approach: | They propose a language-agnostic approach to integrate universal syntax into language models . they use SYntax-aware networks and a COunterfactual training method . |
| Outcome: | The proposed model achieves state-of-the-art performance on natural language inference and question answering without auxiliary training data. |
Translate-Train Embracing Translationese Artifacts (2022.acl-short)
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| Challenge: | Existing approaches to train multilingual tasks are based on translationese and translatetrain. |
| Approach: | They propose to use translationese to mitigate the gap between the source and target languages to train the translator. |
| Outcome: | The proposed method outperforms baselines on the multilingual QA dataset TyDiQA. |
VortexPIA: Indirect Prompt Injection Attack against LLMs for Efficient Extraction of User Privacy (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) have been widely deployed in Conversational AIs . however, the methods proposed in the study rely on a white-box setting . |
| Approach: | They propose an indirect prompt injection attack that induces privacy extraction in LLMs . they use token-efficient data containing false memories to inject LLM data . |
| Outcome: | The proposed method outperforms baselines and achieves state-of-the-art performance. |
Sparse-to-Dense: A Free Lunch for Lossless Acceleration of Video Understanding in LLMs (2025.acl-short)
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| Challenge: | Recent advances in Video Large Language Models (Video-LLMs) have achieved exceptional performance on tasks like video question answering and captioning. |
| Approach: | They propose a decoding strategy that leverages sparse top-K attention and dense full attention to accelerate Video-LLMs without loss. |
| Outcome: | The proposed approach achieves a 1.94 walltime speedup in video processing. |