  Large language models (LLMs) with billions of parameters and pretrained on
massive amounts of data are now capable of near or better than state-of-the-art
performance in a variety of downstream natural language processing tasks.
Neural machine translation (NMT) is one such task that LLMs have been applied
to with great success. However, little research has focused on applying LLMs to
the more difficult subset of NMT called simultaneous translation (SimulMT),
where translation begins before the entire source context is available to the
model. In this paper, we address key challenges facing LLMs fine-tuned for
SimulMT, validate classical SimulMT concepts and practices in the context of
LLMs, explore adapting LLMs that are fine-tuned for NMT to the task of SimulMT,
and introduce Simul-LLM, the first open-source fine-tuning and evaluation
pipeline development framework for LLMs focused on SimulMT.
