标题： 基于预训练语言模型的自适应小样本提示方法在机器翻译中的应用 显示英文标题

标题： Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models

摘要： 最近，具有上下文学习能力的大语言模型（LLMs）在处理神经机器翻译方面表现出显著的潜力。 然而，现有证据表明，LLMs对提示敏感，将固定提示应用于任何输入以进行下游机器翻译任务是次优的。 为了解决这个问题，我们提出了一种自适应少量示例提示（AFSP）框架，以自动选择适合各种源输入句子的翻译示例，从而进一步激发LLM的翻译能力，以实现更好的机器翻译。 首先，我们基于LLM的嵌入构建一个翻译示例检索模块，从对齐的平行翻译语料库中检索出前k个语义相似的翻译示例。 而不是使用其他嵌入模型进行语义示例检索，我们基于部署的LLM的嵌入层构建了一个混合示例检索模块，以构建更好的输入表示，从而检索出更多语义相关的翻译示例。 然后，为了确保源输入和目标输出之间的更好语义一致性，我们借助翻译示例让部署的LLM本身生成多个目标语言的输出候选，并对这些候选进行重新排序。 此外，为了更好地评估我们的AFSP框架在最新语言上的有效性并扩展神经机器翻译的研究边界，我们构建了一个高质量的外交中英平行数据集，该数据集包含5,528个中英平行句子。 最后，在提出的外交中英平行数据集和联合国平行语料库（中英部分）上的大量实验展示了我们提出的AFSP的有效性和优越性。 显示英文摘要

摘要： Recently, Large language models (LLMs) with in-context learning have demonstrated remarkable potential in handling neural machine translation. However, existing evidence shows that LLMs are prompt-sensitive and it is sub-optimal to apply the fixed prompt to any input for downstream machine translation tasks. To address this issue, we propose an adaptive few-shot prompting (AFSP) framework to automatically select suitable translation demonstrations for various source input sentences to further elicit the translation capability of an LLM for better machine translation. First, we build a translation demonstration retrieval module based on LLM's embedding to retrieve top-k semantic-similar translation demonstrations from aligned parallel translation corpus. Rather than using other embedding models for semantic demonstration retrieval, we build a hybrid demonstration retrieval module based on the embedding layer of the deployed LLM to build better input representation for retrieving more semantic-related translation demonstrations. Then, to ensure better semantic consistency between source inputs and target outputs, we force the deployed LLM itself to generate multiple output candidates in the target language with the help of translation demonstrations and rerank these candidates. Besides, to better evaluate the effectiveness of our AFSP framework on the latest language and extend the research boundary of neural machine translation, we construct a high-quality diplomatic Chinese-English parallel dataset that consists of 5,528 parallel Chinese-English sentences. Finally, extensive experiments on the proposed diplomatic Chinese-English parallel dataset and the United Nations Parallel Corpus (Chinese-English part) show the effectiveness and superiority of our proposed AFSP.