18 citations · 94 across the 22 of their papers we have counts for
6 papers · 2 filters
Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning
Ming Li, Lichang Chen, Jiuhai Chen +4
Recent advancements in Large Language Models (LLMs) have expanded the horizons of natural language understanding and generation. Notably, the output control and alignment with the…
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
Ming Li, Yong Zhang, Zhitao Li +6
In the realm of Large Language Models (LLMs), the balance between instruction data quality and quantity is a focal point. Recognizing this, we introduce a self-guided methodology f…
Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection
Jun Yan, Vikas Yadav, Shiyang Li +6
Instruction-tuned Large Language Models (LLMs) have become a ubiquitous platform for open-ended applications due to their ability to modulate responses based on human instructions.…
AlpaGasus: Training A Better Alpaca with Fewer Data
Lichang Chen, Shiyang Li, Jun Yan +8
Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT data…
Backdoor Learning on Sequence to Sequence Models
Lichang Chen, Minhao Cheng, Heng Huang
Backdoor learning has become an emerging research area towards building a trustworthy machine learning system. While a lot of works have studied the hidden danger of backdoor attac…
PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer
Lichang Chen, Heng Huang, Minhao Cheng
Recent studies show that prompt tuning can better leverage the power of large language models than fine-tuning on downstream natural language understanding tasks. However, the exis…