3 papers
cs.LG2024
Collaboratively adding new knowledge to an LLM
Rhui Dih Lee, Laura Wynter
We address the question of how to successively add new knowledge to an LLM whilst retaining previously-added knowledge. We consider two settings, semi-cooperative and fully-coopera…
cs.AI2024
Flexible and Effective Mixing of Large Language Models into a Mixture of Domain Experts
Rhui Dih Lee, Laura Wynter, Raghu Kiran Ganti
We present a toolkit for creating low-cost Mixture-of-Domain-Experts (MOE) from trained models. The toolkit can be used for creating a mixture from models or from adapters. We perf…
cs.LG2024
Enhancing Training Efficiency Using Packing with Flash Attention
Achintya Kundu, Rhui Dih Lee, Laura Wynter +2
Padding is often used in tuning LLM models by adding special tokens to shorter training examples to match the length of the longest sequence in each batch. While this ensures unifo…