Papers by Ze Lin
Do VLMs Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models (2026.findings-acl)
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Zhining Liu, Tianyi Wang, Xiao Lin, Penghao Ouyang, Gaotang Li, Ze Yang, Hui Liu, Sumit Keswani, Vishwa Pardeshi, Huijun Zhao, Wei Fan, Hanghang Tong
| Challenge: | Vision-Language Models (VLMs) have advanced multimodal learning, driving progress in cross-modal reasoning. |
| Approach: | They propose to examine moral robustness of vision-language models by analyzing their moral stances under multimodal perturbations. |
| Outcome: | The proposed model-agnostic multimodal perturbations expose VLMs to a variety of moral vulnerabilities, including a sycophancy trade-off where stronger instruction-following models are more susceptible to persuasion. |
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks (2022.findings-naacl)
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Bill Yuchen Lin, Chaoyang He, Zihang Ze, Hulin Wang, Yufen Hua, Christophe Dupuy, Rahul Gupta, Mahdi Soltanolkotabi, Xiang Ren, Salman Avestimehr
| Challenge: | Increasing concerns and regulations about data privacy necessitate the study of privacy-preserving, decentralized learning methods for natural language processing tasks. |
| Approach: | They propose a framework for evaluating federated learning methods on four different tasks . they propose federation between Transformer-based language models and FL methods . |
| Outcome: | The proposed framework compares FL methods on four different tasks under non-IID partitioning strategies. |
NeuronBlocks: Building Your NLP DNN Models Like Playing Lego (D19-3)
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| Challenge: | Deep Neural Networks (DNN) have been widely employed in industry to address various natural language processing tasks. |
| Approach: | They propose an NLP toolkit that encapsulates neural network modules as building blocks to construct various DNN models with complex architecture. |
| Outcome: | The proposed toolkit can build, train, and test various DNN models with complex architecture. |
MMAPS: End-to-End Multi-Grained Multi-Modal Attribute-Aware Product Summarization (2024.lrec-main)
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| Challenge: | Existing product summarization methods lack end-to-end product summaries and multi-grained multi-modal modeling. |
| Approach: | They propose an end-to-end multi-grained multi-modal attribute-aware product summarization method that jointly models product attributes and generates product summaries. |
| Outcome: | The proposed method outperforms state-of-the-art product summarization methods on a large-scale Chinese e-commence dataset. |