2 citations · 2 across the 3 of their papers we have counts for
4 papers
TCIA: A Task-Centric Instruction Augmentation Method for Instruction Finetuning
Simin Ma, Shujian Liu, Jun Tan +7
Diverse instruction data is vital for effective instruction tuning of large language models, as it enables the model to generalize across different types of inputs . Building such…
Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchy
Tong Wu, Shujian Zhang, Kaiqiang Song +7
Large Language Models (LLMs) are susceptible to security and safety threats, such as prompt injection, prompt extraction, and harmful requests. One major cause of these vulnerabili…
SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe
Yuxin Xiao, Shujian Zhang, Wenxuan Zhou +2
To acquire instruction-following capabilities, large language models (LLMs) undergo instruction tuning, where they are trained on instruction-response pairs using next-token predic…
WPO: Enhancing RLHF with Weighted Preference Optimization
Wenxuan Zhou, Ravi Agrawal, Shujian Zhang +5
Reinforcement learning from human feedback (RLHF) is a promising solution to align large language models (LLMs) more closely with human values. Off-policy preference optimization,…