The evolution of Neural Machine Translation (NMT) has been significantly
influenced by six core challenges (Koehn and Knowles, 2017), which have acted
as benchmarks for progress in this field. This study revisits these challenges,
offering insights into their ongoing relevance in the context of advanced Large
Language Models (LLMs): domain mismatch, amount of parallel data, rare word
prediction, translation of long sentences, attention model as word alignment,
and sub-optimal beam search. Our empirical findings indicate that LLMs
effectively lessen the reliance on parallel data for major languages in the
pretraining phase. Additionally, the LLM-based translation system significantly
enhances the translation of long sentences that contain approximately 80 words
and shows the capability to translate documents of up to 512 words. However,
despite these significant improvements, the challenges of domain mismatch and
prediction of rare words persist. While the challenges of word alignment and
beam search, specifically associated with NMT, may not apply to LLMs, we
identify three new challenges for LLMs in translation tasks: inference
efficiency, translation of low-resource languages in the pretraining phase, and
human-aligned evaluation. The datasets and models are released at
this https URL

神经机器翻译（NMT）的进化受到六个核心挑战的显著影响，本研究重新审视这些挑战，提供有关高级大型语言模型（LLMs）背景下其持续相关性的见解：领域不匹配、平行数据量、罕见词预测、长句翻译、注意力模型作为词对齐以及次优束搜索，我们的实证发现表明 LLMs 在主要语言的预训练阶段有效减少对平行数据的依赖，LLM 基于的翻译系统显著提高了包含大约 80 个单词的长句翻译，并且可翻译长度达 512 个单词的文档。然而，尽管取得了显著改进，领域不匹配和罕见词预测的挑战仍然存在。虽然与 NMT 特定相关的词对齐和束搜索的挑战可能不适用于 LLMs，但我们确定了 LLMs 在翻译任务中面临三个新的挑战：推理效率、预训练阶段对低资源语言的翻译以及人对齐评估。数据集和模型在此 https URL 上发布。