2 citations · 4 across the 13 of their papers we have counts for
5 papers · 2 filters
FlipGuard: Defending Preference Alignment against Update Regression with Constrained Optimization
Mingye Zhu, Yi Liu, Quan Wang +2
Recent breakthroughs in preference alignment have significantly improved Large Language Models' ability to generate texts that align with human preferences and values. However, cur…
ELDER: Enhancing Lifelong Model Editing with Mixture-of-LoRA
Jiaang Li, Quan Wang, Zhongnan Wang +2
Large language models (LLMs) require model editing to efficiently update specific knowledge within them and avoid factual errors. Most model editing methods are solely designed for…
Feature-Adaptive and Data-Scalable In-Context Learning
Jiahao Li, Quan Wang, Licheng Zhang +2
In-context learning (ICL), which promotes inference with several demonstrations, has become a widespread paradigm to stimulate LLM capabilities for downstream tasks. Due to context…
Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generation
Tianqi Zhong, Zhaoyi Li, Quan Wang +4
Compositional generalization, representing the model's ability to generate text with new attribute combinations obtained by recombining single attributes from the training data, is…
Benchmarking Large Language Models on Controllable Generation under Diversified Instructions
Yihan Chen, Benfeng Xu, Quan Wang +2
While large language models (LLMs) have exhibited impressive instruction-following capabilities, it is still unclear whether and to what extent they can respond to explicit constra…