Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models

Understand

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.