In the first part we discussed the practical part of deep dive into LLM.
In this part we will talk about key papers that will help in understanding LLM and passing interviews =) But more on that later.
It all starts with the first GPT
Then I recommend reading the paper about InstructGPT. The topic of training with feedback from a person is discussed there.
Then there are a couple of interesting papers:
– SELF-INSTRUCT
– Information Retrieval with Contrastive Learning
Then I recommend that you familiarize yourself with two truly iconic papers: LORA and QLORA, which solve the following problems:
– learning speed
– computing resources
– memory efficiency
Two more equally important paperpers are PPO and DPO. Understanding these works will help in reward modeling.
And finally:
– Switch Transformers – as a base Mixtures of experts
– Mixtral of Experts – as Open Source SOTA
– Llama 2
Happy reading everyone