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Guangyao Dou

4 accepted papers

2025

Avoiding Copyright Infringement via Large Language Model Unlearning

NAACL 2025findings

Pre-trained Large Language Models (LLMs) have demonstrated remarkable capabilities but also pose risks by learning and generating copyrighted material, leading to significant legal and ethical concerns. In real-world scenarios, model owners need to continuously address copyright infringement as new…

2025

Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models

ACL 2025long

Generative models such as Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) trained on massive datasets can lead them to memorize and inadvertently reveal sensitive information, raising ethical and privacy concerns. While some prior works have explored this issue in the conte…

2025

Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench

NAACL 2025long

Generative models such as Large Language Models (LLM) and Multimodal Large Language models (MLLMs) trained on massive web corpora can memorize and disclose individuals’ confidential and private data, raising legal and ethical concerns. While many previous works have addressed this issue in LLM via m…

2024

Towards Safer Large Language Models through Machine Unlearning

ACL 2024findings

The rapid advancement of Large Language Models (LLMs) has demonstrated their vast potential across various domains, attributed to their extensive pretraining knowledge and exceptional generalizability. However, LLMs often encounter challenges in generating harmful content when faced with problematic…