Papers by Huishuai Zhang
De-Anonymization at Scale via Tournament-Style Attribution (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are rapidly gaining widespread adoption in real-world use . authors propose a method for attributing authorship among tens of thousands of candidate texts . |
| Approach: | They propose a large-language-model-based method for attributing authorship among tens of thousands of candidate texts. |
| Outcome: | The proposed method improves accuracy and ranking precision over previous approaches. |
English as Defense Proxy: Mitigating Multilingual Jailbreak via Eliciting English Safety Knowledge (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models excel in many tasks, but their safety guarantees vary by language. |
| Approach: | They propose a unified approach that leverages English as a universal safety anchor. |
| Outcome: | The proposed approach leverages English as defense proxy (E-Proxy) to transfer safety knowledge across languages. |
Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules (2024.emnlp-main)
Copied to clipboard
| Challenge: | Empirical results show that MoMs consistently outperform vanilla transformers . |
| Approach: | They propose an architecture that allows for a mixture-of-modules computation that uses a finite set of modules defined by multi-head attention and feed-forward networks. |
| Outcome: | The proposed architecture outperforms vanilla Transformers and their variants in multiple ways. |
ReMamba: Equip Mamba with Effective Long-Sequence Modeling (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Mamba models demonstrate superior inference efficiency and competitive performance on short-context tasks, but their capacity to comprehend long contexts is limited compared to transformer-based models. |
| Approach: | They propose a model which incorporates selective compression and adaptation techniques within a two-stage re-forward process, incurring minimal additional inference costs overhead. |
| Outcome: | The proposed model improves on the LongBench and L-Eval benchmarks by 3.2 and 1.6 points and attains performance almost on par with same-size transformer models. |
ReasVQA: Advancing VideoQA with Imperfect Reasoning Process (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing approaches to VideoQA often fail when complex reasoning or temporal relationships are involved. |
| Approach: | They propose a method that leverages reasoning processes generated by Multimodal Large Language Models to improve VideoQA models. |
| Outcome: | The proposed method improves VideoQA models on three benchmarks. |
AdamS: Momentum Itself Can Be A Normalizer for LLM Pretraining and Post-training (2025.emnlp-main)
Copied to clipboard
| Challenge: | Empirically, AdamS demonstrates strong performance in various tasks . et al., 2023b): AdamS is efficient, efficient, and model-agnostic. |
| Approach: | They propose a model-agnostic alternative to Adam for large language model pretraining and post-training. |
| Outcome: | The proposed method matches memory footprint of SGD with momentum while delivering superior performance. |
VideoLLM Knows When to Speak: Enhancing Time-Sensitive Video Comprehension with Video-Text Duet Interaction Format (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies on video large language models focus on model architectures and training datasets . interaction format between user and model is unsatisfactory for time-sensitive tasks . |
| Approach: | They propose a video-text duet interaction format that allows for continuous playback of the video . when a text message ends, the video continues to play, similar to the alternative of two performers in a duet. |
| Outcome: | The proposed format improves performance on time-sensitive tasks with minimal training efforts. |
Shorten After You’re Right: Lazy Length Penalties for Reasoning RL (2026.findings-acl)
Copied to clipboard
Danlong Yuan, Tian Xie, Shaohan Huang, Huishuai Zhang, Zhuocheng Gong, Chong Luo, Furu Wei, Dongyan Zhao
| Challenge: | Existing shortening methods for long reasoning models rely on additional supervision or multi-stage post-training. |
| Approach: | They propose a lazy length penalty that imposes length pressure on models without extra training stages. |
| Outcome: | The proposed method significantly reduces response length without extra training stages while maintaining or improving performance. |
Efficient Domain Continual pretraining by Mitigating the Stability Gap (2025.acl-long)
Copied to clipboard
| Challenge: | Continual pretraining is an important approach for Large Language Models to improve their performance in target domains, learn new topics and languages, and even boost their general capabilities. |
| Approach: | They propose a training strategy that mitigates instability by increasing the number of epochs, along with two data sampling strategies targeting data domain relevance and corpus distribution. |
| Outcome: | The proposed training strategy improves the average medical task performance of the OpenLlama-3B model from 36.2% to 40.7% using only 40% of the original training budget, while also enhancing general task performance without causing forgetting. |
Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models (2026.acl-long)
Copied to clipboard
Xin Cheng, Wangding Zeng, Damai Dai, Qinyu Chen, Bingxuan Wang, Zhenda Xie, Kezhao Huang, Xingkai Yu, Zhewen Hao, Han Zhang, Yu-Kun Li, Huishuai Zhang, Dongyan Zhao, Wenfeng Liang
| Challenge: | Mixture-of-Experts (MoE) scales capacity via conditional computation, but lacks knowledge lookup primitive. |
| Approach: | They propose a conditional memory instantiated via Deep Sparse Embedding (DSE) they propose 'u-shaped scaling law' that identifies optimal balance between MoE experts and DSE memory . |
| Outcome: | The proposed model outperforms an iso-parameter and isoFLOPs MoE baseline across knowledge and reasoning benchmarks and is infrastructure-efficient. |