Little late to this thread but from my list:
LLM (foundational papers)
* Attention is all you need - transformers + self attention
* BERT - first masked LM using transformers + self attention
* GPT3 - big LLM decoder (Basis of gpt4 and most LLM)
* Instruct GPT or TKInstruct (instruction tuning enables improved zero shot learning)
* Chain of Thought (improve performance via prompting)
some other papers which are become trendy depending on your interest
* RLHF - RL using human feedback
* Lora - make models smaller
* MoE - kind of ensembling
* self instruct - self label data
* constitutional ai - self alignment
* tree of thought - like CoT but a tree
* FastAttention,Longformer - optimized attention mechanisms
* React - agents
LLM (foundational papers)
* Attention is all you need - transformers + self attention
* BERT - first masked LM using transformers + self attention
* GPT3 - big LLM decoder (Basis of gpt4 and most LLM)
* Instruct GPT or TKInstruct (instruction tuning enables improved zero shot learning)
* Chain of Thought (improve performance via prompting)
some other papers which are become trendy depending on your interest
* RLHF - RL using human feedback
* Lora - make models smaller
* MoE - kind of ensembling
* self instruct - self label data
* constitutional ai - self alignment
* tree of thought - like CoT but a tree
* FastAttention,Longformer - optimized attention mechanisms
* React - agents