Papers by Zewen Bai
Make Your Decision Convincing! A Unified Two-Stage Framework: Self-Attribution and Decision-Making (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing frameworks for explaining black-box model behavior are unreliable . large-scale pre-trained models often rely on superficial clues for predictions . |
| Approach: | They propose a unified two-stage framework that uses subsequences from the input text as a rationale to generate model decision. |
| Outcome: | The proposed framework achieves competitive results on five reasoning datasets and in semi-supervised scenarios. |
HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference (2025.findings-acl)
Copied to clipboard
Ping Gong, Jiawei Yi, Shengnan Wang, Juncheng Zhang, Zewen Jin, Ouxiang Zhou, Ruibo Liu, Guanbin Xu, Youhui Bai, Bowen Ye, Kun Yuan, Tong Yang, Gong Zhang, Renhai Chen, Feng Wu, Cheng Li
| Challenge: | Existing top-k attention methods struggle to strike a balance between efficiency and accuracy. |
| Approach: | They propose a top-k attention approach that integrates low-overhead techniques into the Top-k Attention process to achieve 7.2 speedup compared to vanilla full attention. |
| Outcome: | The proposed approach achieves 7.2 speedup compared to current top-k attention methods while maintaining model accuracy. |
STATE ToxiCN: A Benchmark for Span-level Target-Aware Toxicity Extraction in Chinese Hate Speech Detection (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on Chinese hate speech detection lack span-level fine-grained annotations. |
| Approach: | They construct a Span-level target-aware Toxicity Extraction dataset and evaluate existing models for Chinese hateful slang. |
| Outcome: | The proposed dataset is the first span-level Chinese hate speech dataset and evaluates the ability of existing models to understand hate semantics. |
Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition (2025.naacl-long)
Copied to clipboard
| Challenge: | Current methods for humor recognition focus on one aspect of humor commonalities, ignoring the multifaceted nature of humor. |
| Approach: | They propose a commonality and individuality incorporated network for humor recognition that integrates multifaceted humor commonalities with speaker individuality. |
| Outcome: | The proposed model integrates multifaceted humor commonalities with speaker individuality to deepen the understanding of humor expressions. |