Papers by Zeyu Fu
Unlocking Emergent Modularity in Large Language Models (2024.naacl-long)
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| Challenge: | Existing MNNs are explicit, with predefined modular architectures and individual modules expected to implement distinct functions. |
| Approach: | They propose to unlock emergent modularity in language models by fine-tuning them as Mixture-of-Experts (MoEs) EMoE is robust to various configurations and can scale up to Large Language Models . |
| Outcome: | The proposed models can be fine-tuned as Mixture-of-Expert (MoE) counterparts without introducing any extra parameters. |
A Closer Look into Mixture-of-Experts in Large Language Models (2025.findings-naacl)
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| Challenge: | Mixture-of-experts (MoE) architectures are gaining increasing attention for their unique properties and remarkable performance. |
| Approach: | They propose a mixture-of-experts architecture that allows for model scaling without sacrificing computational efficiency. |
| Outcome: | The proposed model increases model size without sacrificing computational efficiency . the proposed model is modular and can be used by a broad spectrum of practitioners . |
Multi-Docker-Eval: A ‘Shovel of the Gold Rush’ Benchmark on Automatic Environment Building for Software Engineering (2026.findings-acl)
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| Challenge: | Automated environment configuration is a critical bottleneck in scaling software engineering (SWE) automation. |
| Approach: | They propose a reliable evaluation standard for automated environment configuration for 40 real-world repositories spanning 9 programming languages. |
| Outcome: | The proposed benchmark includes 40 real-world repositories spanning 9 programming languages and measures success in achieving executable states and efficiency under realistic constraints. |
Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies (2025.naacl-long)
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Zirui Song, Guangxian Ouyang, Meng Fang, Hongbin Na, Zijing Shi, Zhenhao Chen, Fu Yujie, Zeyu Zhang, Shiyu Jiang, Miao Fang, Ling Chen, Xiuying Chen
| Challenge: | Existing household robots are inability to recognize potential problems or dangers in home environments. |
| Approach: | They propose a task of creating anomaly scenarios using generative models instead of manually labeled data to build simulated environments. |
| Outcome: | The proposed framework outperforms existing models in terms of task description and scene diversity. |
Unlocking Continual Learning Abilities in Language Models (2024.findings-emnlp)
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| Challenge: | Existing approaches to learning models (LMs) incorporate old task data or task-wise inductive bias into LMs, but old data and accurate task information are often unavailable or costly to collect. |
| Approach: | They propose a rehearsal-free method that updates model parameters with large magnitudes . they found that the L1-normalized magnitude distribution is different when different task data is used . |
| Outcome: | The proposed method improves accuracy and performance on four CL benchmarks. |
GraphRAG-Rad: Concept-Aware Radiology Report Generation via Latent Visual-Semantic Retrieval (2026.eacl-srw)
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| Challenge: | Existing encoder-decoder models suffer from hallucinations, generating plausible but incorrect medical findings. |
| Approach: | They propose a novel architecture that integrates biomedical knowledge through a latent visual-semantic retrieval approach. |
| Outcome: | The proposed architecture achieves competitive performance with strong results across multiple metrics. |