Simultaneous Machine Translation with Large Language Models

Understand

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.