Fine-tuning and RLHF#
Slides from the lecture introducing different fine-tuning techniques (PEFT, instruction fine-tuning, RL-based fine-tuning) can be found here.
Additional materials#
If you want to dig a bit deeper, here are (optional!) supplementary readings on some of the topics covered in class:
Supervised fine-tuning:
Ding et al. (2023) Parameter-efficient fine-tuning of large-scale pre-trained language models
Howard et al. (2018) Universal Language Model Fine-tuning for Text Classification
background paper rather than PEFT-related
RLHF: