Conversational SimulMT: Efficient Simultaneous Translation with Large
  Language Models
Minghan Wang Thuy-Trang Vu Ehsan Shareghi Gholamreza Haffari
Simultaneous machine translation (SimulMT) presents a challenging trade-off
between translation quality and latency. Recent studies have shown that LLMs
can achieve good performance in SimulMT tasks. However, this often comes at the
expense of high inference cost and latency. In this paper, we propose a
conversational SimulMT framework to enhance the inference efficiency of
LLM-based SimulMT through multi-turn-dialogue-based decoding. Our experiments
with Llama2-7b-chat on two SimulMT benchmarks demonstrate the superiority of
LLM in translation quality while achieving comparable computational latency to
specialized SimulMT models.