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Cenyuan Zhang

7 accepted papers

2024

Advancing Parameter Efficiency in Fine-tuning via Representation Editing

ACL 2024long

Parameter Efficient Fine-Tuning (PEFT) has gained significant attention for its ability to achieve competitive results while updating only a small subset of trainable parameters. Despite the promising performance of current PEFT methods, they present challenges in hyperparameter selection, such as d…

2024

Aligning Large Language Models with Human Preferences through Representation Engineering

ACL 2024long

Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness. Existing methods for achieving this alignment often involve employing reinforcement learning from human feedback (RLHF…

2024

Promoting Data and Model Privacy in Federated Learning through Quantized LoRA

EMNLP 2024finding

Conventional federated learning primarily aims to secure the privacy of data distributed across multiple edge devices, with the global model dispatched to edge devices for parameter updates during the learning process. However, the development of large language models (LLMs) requires substantial dat…

Cited by 3SourcePDFScholar
2023

Watermarking PLMs on Classification Tasks by Combining Contrastive Learning with Weight Perturbation

EMNLP 2023long findings

Large pre-trained language models (PLMs) have achieved remarkable success, making them highly valuable intellectual property due to their expensive training costs. Consequently, model watermarking, a method developed to protect the intellectual property of neural models, has emerged as a crucial yet…

Cited by 0SourceScholar
2022

Improving the Adversarial Robustness of NLP Models by Information Bottleneck

ACL 2022findings

Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manipulated by adversaries to fool NLP models. In this study, we explore the feasibility of capturing task-specific robust fe…

2022

Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples

ACL 2022findings

Most of the existing defense methods improve the adversarial robustness by making the models adapt to the training set augmented with some adversarial examples. However, the augmented adversarial examples may not be natural, which might distort the training distribution, resulting in inferior perfor…

Cited by 10SourcePDFScholar
2021

Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models

ACL 2021short

A sequence-to-sequence learning with neural networks has empirically proven to be an effective framework for Chinese Spelling Correction (CSC), which takes a sentence with some spelling errors as input and outputs the corrected one. However, CSC models may fail to correct spelling errors covered by…