5 citations · 13 across the 5 of their papers we have counts for
3 papers · 1 filter
Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs
Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang +10
The rise of large language models (LLMs) has created a significant disparity: industrial research labs with their computational resources, expert teams, and advanced infrastructure…
LAB: Large-Scale Alignment for ChatBots
Shivchander Sudalairaj, Abhishek Bhandwaldar, Aldo Pareja +3
This work introduces LAB (Large-scale Alignment for chatBots), a novel methodology designed to overcome the scalability challenges in the instruction-tuning phase of large language…
Curiosity-driven Red-teaming for Large Language Models
Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang +5
Large language models (LLMs) hold great potential for many natural language applications but risk generating incorrect or toxic content. To probe when an LLM generates unwanted con…