7 citations · 8 across the 6 of their papers we have counts for
6 papers
De-attribute to Forget for LLM Unlearning
Xinyang Lu, Jiabao Pan, Rachael Hwee Ling Sim +3
The rapid development of large language models (LLMs) has raised concerns on the use of inappropriate data for training, which has led to a growing interest in LLM unlearning. Many…
WaterDrum: Watermarking for Data-centric Unlearning Metric
Xinyang Lu, Xinyuan Niu, Gregory Kang Ruey Lau +7
Large language model (LLM) unlearning is critical in real-world applications where it is necessary to efficiently remove the influence of private, copyrighted, or harmful data from…
Global-to-Local Support Spectrums for Language Model Explainability
Lucas Agussurja, Xinyang Lu, Bryan Kian Hsiang Low
Existing sample-based methods, like influence functions and representer points, measure the importance of a training point by approximating the effect of its removal from training.…
TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs
Cheng Wang, Xinyang Lu, See-Kiong Ng +1
The rapid evolution of large language models (LLMs) represents a substantial leap forward in natural language understanding and generation. However, alongside these advancements co…
On Newton's Method to Unlearn Neural Networks
Nhung Bui, Xinyang Lu, Rachael Hwee Ling Sim +2
With the widespread applications of neural networks (NNs) trained on personal data, machine unlearning has become increasingly important for enabling individuals to exercise their…
Source Attribution for Large Language Model-Generated Data
Jingtan Wang, Xinyang Lu, Zitong Zhao +4
The impressive performances of Large Language Models (LLMs) and their immense potential for commercialization have given rise to serious concerns over the Intellectual Property (IP…