通过增强示例语义多样性，提升少样本机器翻译的准确性
Enhancing Contrastive Demonstration Selection with Semantic Diversity for Robust In-Context Machine Translation
- 在对比选择基础上引入嵌入空间差异性，提升示例多样性
- 在4种语言对上1/3样本设置下均超越基线方法
- 适合追求高鲁棒性的少样本翻译场景
上下文学习（ICL）使大语言模型通过少量输入输出示例完成任务，但其性能高度依赖示例选择。现有方法多关注相似性或对比选择，忽视示例间的多样性。本文提出DiverseConE（语义多样性增强的对比示例选择），在对比选择基础上引入基于嵌入空间差异性的多样性增强步骤。我们在Llama2-7b模型上针对英中、中英、俄德、德俄4种语言对，在1-shot和3-shot设置下进行实验，使用COMET20和COMET22评估。结果表明，DiverseConE持续优于随机选择、BM25、TopK及当前最优对比选择方法。进一步分析包括多样性度量与人工评估，验证了该方法有效性，并凸显考虑示例多样性对提升翻译质量的关键作用。
原文摘要 · Abstract (English)
In-Context Learning (ICL) empowers large language models to perform tasks by conditioning on a few input-output examples. However, the performance of ICL is highly sensitive to the selection of these demonstrations. While existing methods focus on similarity or contrastive selection, they often overlook the importance of diversity among the chosen examples. In this paper, we propose DiverseConE (Diversity-Enhanced Contrastive Example Selection), a novel approach for demonstration selection in in-context learning for machine translation. Our method builds upon contrastive selection by incorporating a diversity enhancement step based on embedding space dissimilarity. We conduct extensive experiments on the Llama2-7b model across four language pairs (English-Chinese, Chinese-English, Russian-German, German-Russian) in 1-shot and 3-shot settings, using COMET20 and COMET22 for evaluation. Our results demonstrate that DiverseConE consistently outperforms strong baseline methods, including random selection, BM25, TopK, and a state-of-the-art contrastive selection method. Further analysis, including diversity metrics and human evaluation, validates the effectiveness of our approach and highlights the benefits of considering demonstration diversity for improved translation quality.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。