Papers by Zhe Fu
Advancing Vision-Language Models with Adapter Ensemble Strategies (2024.findings-emnlp)
Copied to clipboard
| Challenge: | CLIP revolutes vision-language pretraining by using contrastive learning on paired web data. |
| Approach: | They propose to combine a "adapter ensemble" with traditional machine learning techniques to augment large-scale pretrained vision-language models. |
| Outcome: | The proposed model outperforms baselines and derives improvement when the number of ensemble parameters increases. |
Context-Aware Interaction Network for Question Matching (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing models focus on word-level local matching and neglect the importance of contextual information. |
| Approach: | They propose a context-aware interaction network to properly align two sequences and infer their semantic relationship by using gate fusion layers. |
| Outcome: | The proposed model can accurately align two sequences and infer their semantic relationship on two question matching datasets. |
How to Make LMs Strong Node Classifiers? (2026.findings-eacl)
Copied to clipboard
Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong
| Challenge: | Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs). |
| Approach: | They propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the art (SOTA) GNNs on node classification tasks without requiring any architectural modifications. |
| Outcome: | The proposed approach outperforms existing GNNs on node classification tasks and is open-source upon publication. |
Fine-tuning LLMs with Cross-Attention-based Weight Decay for Bias Mitigation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) excel in natural language processing tasks but often propagate societal biases from their training data, leading to discriminatory outputs. |
| Approach: | They propose a method that modifies the LLM architecture to mitigate bias by adjusting the attention weights of sensitive tokens. |
| Outcome: | The proposed method can handle multiple sensitive attributes and does not require full knowledge of sensitive tokens presented in the dataset. |
RFBFN: A Relation-First Blank Filling Network for Joint Relational Triple Extraction (2022.acl-srw)
Copied to clipboard
| Challenge: | Existing methods for relational triple extraction ignore semantic information of relations or predict subjects and objects sequentially. |
| Approach: | They propose a relation-first blank filling network to capture semantic information of relations . they transform relations into relation templates with blanks which contain the fine-grained semantic representation of relations. |
| Outcome: | The proposed model outperforms current state-of-the-art methods on public benchmark datasets. |
Fair RAG: End-to-End Fairness Across Retrieval and Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) can amplify demographic bias by generating skewed context . prior work treats fairness in retrieval or generation in isolation, leaving end-to-end fairness underexplored . |
| Approach: | They propose a pipeline that jointly controls both retrieval and generation stages . large language models can handle a broad set of inference tasks, they argue . |
| Outcome: | The proposed pipeline reduces retriever-side skew and achieves lowest generator-side disparity while preserving utility. |