3 papers
cs.CL2024
A Gradient Analysis Framework for Rewarding Good and Penalizing Bad Examples in Language Models
Yi-Lin Tuan, William Yang Wang
Beyond maximum likelihood estimation (MLE), the standard objective of a language model (LM) that optimizes good examples probabilities, many studies have explored ways that also pe…
cs.CL2024
Towards Safety and Helpfulness Balanced Responses via Controllable Large Language Models
Yi-Lin Tuan, Xilun Chen, Eric Michael Smith +5
As large language models (LLMs) become easily accessible nowadays, the trade-off between safety and helpfulness can significantly impact user experience. A model that prioritizes s…
cs.LG2024
Dynamic Latent Separation for Deep Learning
Yi-Lin Tuan, Zih-Yun Chiu, William Yang Wang
A core problem in machine learning is to learn expressive latent variables for model prediction on complex data that involves multiple sub-components in a flexible and interpretabl…