Papers by Zhaoye Li
Balanced Data Sampling for Language Model Training with Clustering (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) are a fundamental part of the training process. |
| Approach: | They propose to use clustering to balance the text distribution of training data for better model training. |
| Outcome: | Extensive experiments validate the effectiveness of ClusterClip Sampling under various training datasets and large language models. |
WESR: A Benchmark and Strong Baseline for Word-level Event-Speech Recognition (2026.findings-acl)
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Chenchen Yang, Kexin Huang, Liwei Fan, Qian Tu, Botian Jiang, Dong Zhang, Linqi Yin, Shimin Li, Zhaoye Fei, Qinyuan Cheng, Xipeng Qiu
| Challenge: | aaron carroll: the precise localization of non-verbal vocal events remains a critical yet under-explored challenge. carroll says current methods suffer from insufficient task definitions with limited category coverage. carrol: knowing exactly where an event occurred is not enough; knowing exactly what it happened is. |
| Approach: | They propose a taxonomy of 21 vocal events with a new categorization into discrete versus continuous types. |
| Outcome: | The proposed model disentangles ASR errors from event detection while maintaining ASR quality. |
Turn Waste into Worth: Rectifying Top-k Router of MoE (2024.emnlp-main)
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Zhiyuan Zeng, Qipeng Guo, Zhaoye Fei, Zhangyue Yin, Yunhua Zhou, Linyang Li, Tianxiang Sun, Hang Yan, Dahua Lin, Xipeng Qiu
| Challenge: | Top-k router suffers from redundancy computation and memory costs due to unbalanced routing . some experts are overflow, where exceeding tokens are dropped, while others are empty, which are padded with zeros, negatively impacting model performance. |
| Approach: | They propose a top-k router that is unbalanced and uses a multi-gPU system to handle dropped tokens and padding. |
| Outcome: | The proposed model surpasses the top-1 router by 4.7% in terms of performance . the top-k router suffers from redundancy computation and memory costs . |
XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs (2026.acl-long)
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Yitian Gong, Luozhijie Jin, Kuangwei Chen, Dong Zhang, Ruifan Deng, Xiaogui Yang, Xin Zhang, Zhaoye Fei, Qinyuan Cheng, Shimin Li, Xipeng Qiu
| Challenge: | Existing speech codecs struggle to balance these objectives at low bitrates . XY-Tokenizer achieves stronger semantic alignment than representative semantic-distillation codec . |
| Approach: | They propose a low-bitrate speech codec that aligns discrete speech representations with text while preserving fine-grained acoustic details for reconstruction. |
| Outcome: | The proposed codec outperforms existing low-bitrate speech codecs in speech understanding and generation tasks. |
JPU: Bridging Jailbreak Defense and Unlearning via On-Policy Path Rectification (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are vulnerable to diverse jailbreak attacks despite extensive safety alignment . |
| Approach: | They propose a method to rectify dynamic jailbreak paths towards safety anchors by dynamically mining on-policy adversarial samples to expose vulnerabilities and identify jailbreak path. |
| Outcome: | The proposed model significantly improves jailbreak resistance against dynamic attacks while maintaining its utility. |