Papers by Quan Zhou
Alignment-Enhanced Decoding: Defending Jailbreaks via Token-Level Adaptive Refining of Probability Distributions (2024.emnlp-main)
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| Challenge: | Existing defenses against jailbreaks focus on perturbing or inspecting inputs, but ignore competing objectives, the underlying cause of alignment failures. |
| Approach: | They propose a novel defense that employs adaptive decoding to address the root causes of jailbreak issues. |
| Outcome: | The proposed defense improves safety alignment while maintaining helpfulness. |
Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation (2024.lrec-main)
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| Challenge: | Previous Sign Language Translation methods have relied on gloss annotations to improve performance, but labeling high-quality glosses is labor-intensive and inefficient. |
| Approach: | They propose to integrate Large Language Model (LLM) into SLT by factorizing learning into two stages to improve the learning curve. |
| Outcome: | The proposed approach improves on three SLT datasets conducted under the gloss-free setting. |
PEPE: Long-context Extension for Large Language Models via Periodic Extrapolation Positional Encodings (2025.findings-emnlp)
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Jikun Hu, Dongsheng Guo, Yuli Liu, Qingyao Ai, Lixuan Wang, Xuebing Sun, Qilei Zhang, Quan Zhou, Cheng Luo
| Challenge: | Long-context extension attempts to extend contextual window in pre-trained LLMs . primary method involves expanding initial positional encodings, disrupting positional learning . |
| Approach: | They propose a new extension strategy based on Rotary Position Embedding to extend contextual window in pre-trained large language models. |
| Outcome: | The proposed method can extend the contextual window in pre-trained large language models . expansion disrupts positional encodings learned during pre-training, authors show . |
A Probabilistic Framework for Discovering New Intents (2023.acl-long)
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| Challenge: | Existing methods for detecting unknown intents do not explore the intrinsic structure of unlabeled data. |
| Approach: | They propose a probabilistic framework for discovering intents where intent assignments are treated as latent variables. |
| Outcome: | The proposed framework can be used to discover intents with latent variables . it can be applied to three challenging real-world datasets . |
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)
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Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang
| Challenge: | Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). |
| Approach: | They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE. |
| Outcome: | The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency. |
DMSD: Dual-Modal Semantic Disentanglement for Compositional Zero-Shot Learning (2026.findings-acl)
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| Challenge: | Compositional Zero-Shot Learning (CZSL) is a new research paradigm that learns sub-concepts from seen compositions and recognizes unseen novel combinations. |
| Approach: | They propose a Dual-Modal Semantic Disentanglement framework that integrates visual and textual information to achieve effective sub-concept disentangling. |
| Outcome: | The proposed framework achieves state-of-the-art performance on three benchmark datasets . it integrates a class-centroid bridge module to guide class centroids toward the textual space . |
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension (D19-58)
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Hongyu Li, Xiyuan Zhang, Yibing Liu, Yiming Zhang, Quan Wang, Xiangyang Zhou, Jing Liu, Hua Wu, Haifeng Wang
| Challenge: | MRC requires machines to understand text and answer questions about the text. |
| Approach: | They propose a simple system Baidu submitted for MRQA 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models. |
| Outcome: | The proposed system is ranked at top 1 of all participants in terms of averaged F1 score. |
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability (2025.findings-acl)
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| Challenge: | Existing studies have shown that training language models with rationales augmentation is beneficial, but this view does not hold consistently. |
| Approach: | They conduct comprehensive investigations to thoroughly inspect the impact of rationales on model performance and a novel perspective of model reliability. |
| Outcome: | The proposed method outperforms untrained models in several areas and provides informative regulations on the broad utilization of rationales. |