5 citations · 8 across the 3 of their papers we have counts for
3 papers
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…
DAP: Domain-aware Prompt Learning for Vision-and-Language Navigation
Ting Liu, Yue Hu, Wansen Wu +3
Following language instructions to navigate in unseen environments is a challenging task for autonomous embodied agents. With strong representation capabilities, pretrained vision-…