  Real-world simultaneous machine translation (SimulMT) systems face more
challenges than just the quality-latency trade-off. They also need to address
issues related to robustness with noisy input, processing long contexts, and
flexibility for knowledge injection. These challenges demand models with strong
language understanding and generation capabilities which may not often equipped
by dedicated MT models. In this paper, we investigate the possibility of
applying Large Language Models (LLM) to SimulMT tasks by using existing
incremental-decoding methods with a newly proposed RALCP algorithm for latency
reduction. We conducted experiments using the \texttt{Llama2-7b-chat} model on
nine different languages from the MUST-C dataset. The results show that LLM
outperforms dedicated MT models in terms of BLEU and LAAL metrics. Further
analysis indicates that LLM has advantages in terms of tuning efficiency and
robustness. However, it is important to note that the computational cost of LLM
remains a significant obstacle to its application in SimulMT.\footnote{We will
release our code, weights, and data with publication.}
