GenTranslate: Large Language Models are Generative Multilingual Speech
  and Machine Translators
Yuchen Hu Chen Chen Chao-Han Huck Yang Ruizhe Li Dong Zhang Zhehuai Chen Eng Siong Chng
Recent advances in large language models (LLMs) have stepped forward the
development of multilingual speech and machine translation by its reduced
representation errors and incorporated external knowledge. However, both
translation tasks typically utilize beam search decoding and top-1 hypothesis
selection for inference. These techniques struggle to fully exploit the rich
information in the diverse N-best hypotheses, making them less optimal for
translation tasks that require a single, high-quality output sequence. In this
paper, we propose a new generative paradigm for translation tasks, namely
"GenTranslate", which builds upon LLMs to generate better results from the
diverse translation versions in N-best list. Leveraging the rich linguistic
knowledge and strong reasoning abilities of LLMs, our new paradigm can
integrate the rich information in N-best candidates to generate a
higher-quality translation result. Furthermore, to support LLM finetuning, we
build and release a HypoTranslate dataset that contains over 592K
hypotheses-translation pairs in 11 languages. Experiments on various speech and
machine translation benchmarks (e.g., FLEURS, CoVoST-2, WMT) demonstrate that
our GenTranslate significantly outperforms the state-of-the-art model.