5 papers
What Is Preference Optimization Doing, and Why?
Yue Wang, Qizhou Wang, Zizhuo Zhang +3
Preference optimization (PO) is indispensable for large language models (LLMs), with methods such as direct preference optimization (DPO) and proximal policy optimization (PPO) ach…
Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding
Zhiqin Yang, Yuhan Liu, Jingwen Fu +4
Although natural language is the default medium for Large Language Models (LLMs), its limited expressive capacity creates a profound bottleneck for complex problem-solving. While r…
Learning without Isolation: Pathway Protection for Continual Learning
Zhikang Chen, Abudukelimu Wuerkaixi, Sen Cui +10
Deep networks are prone to catastrophic forgetting during sequential task learning, i.e., losing the knowledge about old tasks upon learning new tasks. To this end, continual learn…
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods
Qizhou Wang, Bo Han, Puning Yang +3
The imperative to eliminate undesirable data memorization underscores the significance of machine unlearning for large language models (LLMs). Recent research has introduced a seri…
Accurate Forgetting for Heterogeneous Federated Continual Learning
Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang +6
Recent years have witnessed a burgeoning interest in federated learning (FL). However, the contexts in which clients engage in sequential learning remain under-explored. Bridging F…