Papers by Libo Wu
Adaptive Policy with Wait-k Model for Simultaneous Translation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to simultaneous machine translation require a robust read/write policy . a standalone multi-path wait-k model performs competitively with adaptive policies . |
| Approach: | They propose a more flexible approach by decoupling the adaptive policy model from the translation model. |
| Outcome: | The proposed approach outperforms baseline approaches in translation tasks. |
OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models (2026.acl-long)
Copied to clipboard
Qiguang Chen, Chengyu Luan, Jiajun Wu, Qiming Yu, Yi Yang, Yizhuo Li, Jingqi Tong, Xiachong Feng, Libo Qin, Wanxiang Che
| Challenge: | Existing multimodal reasoning benchmarks for large vision-language models emphasize single-image analysis and fail to exploit contextual information across multiple images. |
| Approach: | They propose a benchmark to evaluate Olympiad-level reasoning when evidence is distributed over multiple images. |
| Outcome: | The proposed model outperforms existing models on bi-image Olympiads and Gemini-3-Pro on multimodal Olympiad-level reasoning tasks. |
A Structure-Aware Argument Encoder for Literature Discourse Analysis (2022.coling-1)
Copied to clipboard
Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, Libo Wu
| Challenge: | Existing research for argument representation learning treats tokens in sentences equally and ignores the implied structure information of argumentative context. |
| Approach: | They propose to separate tokens into two groups to capture structural information of arguments and to incorporate paragraph-level position information into the model. |
| Outcome: | The proposed model captures structural information of arguments and is able to identify arguments automatically. |
SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing attacks are classified into end-to-end and pre-training types based on the attack phase . Existing backdoor attacks are based upon perplexity, fine-pruning, and maxEntropy. |
| Approach: | They propose an entropy-based poisoning filter that mitigates backdoor attacks . they propose an invisible and universal task-agnostic backdoor attack via syntactic transfer . |
| Outcome: | The proposed attack can transfer backdoors to various downstream tasks while preserving pre-trained language models' pre-training capabilities. |
A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code (2026.findings-acl)
Copied to clipboard
Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, Dong Zhang
| Challenge: | Existing security evaluation benchmarks lack relevance to real-world AI programming tasks . current LLMs struggle with secure coding, research shows . |
| Approach: | They propose a repository-level evaluation benchmark to assess security of AI-generated code. |
| Outcome: | The proposed framework mirrors real-world AI programming tasks and offers valuable insights into the state of AI code generation. |