5 citations · 5 across the 11 of their papers we have counts for
6 papers · 1 filter
On the Loss of Context-awareness in General Instruction Fine-tuning
Yihan Wang, Andrew Bai, Nanyun Peng +1
Pre-trained Large Language Models (LLMs) require post-training methods such as supervised fine-tuning (SFT) on instruction-response pairs to enable instruction following. However,…
CLUE: Concept-Level Uncertainty Estimation for Large Language Models
Yu-Hsiang Wang, Andrew Bai, Che-Ping Tsai +1
Large Language Models (LLMs) have demonstrated remarkable proficiency in various natural language generation (NLG) tasks. Previous studies suggest that LLMs' generation process inv…
Embedding Space Selection for Detecting Memorization and Fingerprinting in Generative Models
Jack He, Jianxing Zhao, Andrew Bai +1
In the rapidly evolving landscape of artificial intelligence, generative models such as Generative Adversarial Networks (GANs) and Diffusion Models have become cornerstone technolo…
Defending LLMs against Jailbreaking Attacks via Backtranslation
Yihan Wang, Zhouxing Shi, Andrew Bai +1
Although many large language models (LLMs) have been trained to refuse harmful requests, they are still vulnerable to jailbreaking attacks which rewrite the original prompt to conc…
An Efficient Rehearsal Scheme for Catastrophic Forgetting Mitigation during Multi-stage Fine-tuning
Andrew Bai, Chih-Kuan Yeh, Cho-Jui Hsieh +1
Incrementally fine-tuning foundational models on new tasks or domains is now the de facto approach in NLP. A known pitfall of this approach is the \emph{catastrophic forgetting} of…
Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation
Tong Xie, Haoyu Li, Andrew Bai +1
Data attribution methods trace model behavior back to its training dataset, offering an effective approach to better understand ''black-box'' neural networks. While prior research…