TransLLaMa: LLM-based Simultaneous Translation System
Roman Koshkin Katsuhito Sudoh Satoshi Nakamura
Decoder-only large language models (LLMs) have recently demonstrated
impressive capabilities in text generation and reasoning. Nonetheless, they
have limited applications in simultaneous machine translation (SiMT), currently
dominated by encoder-decoder transformers. This study demonstrates that, after
fine-tuning on a small dataset comprising causally aligned source and target
sentence pairs, a pre-trained open-source LLM can control input segmentation
directly by generating a special "wait" token. This obviates the need for a
separate policy and enables the LLM to perform English-German and
English-Russian SiMT tasks with BLEU scores that are comparable to those of
specific state-of-the-art baselines. We also evaluated closed-source models
such as GPT-4, which displayed encouraging results in performing the SiMT task
without prior training (zero-shot), indicating a promising avenue for enhancing
future SiMT systems.