论文标题
使用外部资源的T5重新置换器的检索增强
Retrieval Augmentation for T5 Re-ranker using External Sources
论文作者
论文摘要
检索增强已显示出有希望的不同任务的改进。但是，这种增强是否可以帮助基于语言模型的大型重新级别，尚不清楚。我们研究了如何使用从两个外部Corpora（商业网络搜索引擎和Wikipedia）检索到的高质量信息来增强基于T5的重新库。我们从经验上证明，检索增强如何显着提高基于T5的重新列车对内域和零射击的重新排列任务的有效性。
Retrieval augmentation has shown promising improvements in different tasks. However, whether such augmentation can assist a large language model based re-ranker remains unclear. We investigate how to augment T5-based re-rankers using high-quality information retrieved from two external corpora -- a commercial web search engine and Wikipedia. We empirically demonstrate how retrieval augmentation can substantially improve the effectiveness of T5-based re-rankers for both in-domain and zero-shot out-of-domain re-ranking tasks.
