1 citations · 3 across the 5 of their papers we have counts for
5 papers
Fundamental Safety-Capability Trade-offs in Fine-tuning Large Language Models
Pin-Yu Chen, Han Shen, Payel Das +1
Fine-tuning Large Language Models (LLMs) on some task-specific datasets has been a primary use of LLMs. However, it has been empirically observed that this approach to enhancing ca…
SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selection
Han Shen, Pin-Yu Chen, Payel Das +1
Fine-tuning on task-specific data to boost downstream performance is a crucial step for leveraging Large Language Models (LLMs). However, previous studies have demonstrated that fi…
Understanding Forgetting in LLM Supervised Fine-Tuning and Preference Learning -- A Convex Optimization Perspective
Heshan Fernando, Han Shen, Parikshit Ram +4
The post-training of LLMs, which typically consists of the supervised fine-tuning (SFT) stage and the preference learning stage (RLHF or DPO), is crucial to effective and safe LLM…
Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF
Han Shen, Zhuoran Yang, Tianyi Chen
Bilevel optimization has been recently applied to many machine learning tasks. However, their applications have been restricted to the supervised learning setting, where static obj…
Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization
A F M Saif, Xiaodong Cui, Han Shen +3
In this paper, we present a novel bilevel optimization-based training approach to training acoustic models for automatic speech recognition (ASR) tasks that we term {bi-level joint…