论文标题
检索增强的机器学习
Retrieval-Enhanced Machine Learning
论文作者
论文摘要
尽管信息访问系统长期以来一直支持人们完成各种任务，但我们建议扩大信息访问系统的用户范围，以包括任务驱动的机器，例如机器学习模型。通过这种方式，可以应用索引，表示，检索和排名的核心原理，并扩展到实质上改善模型的概括，可伸缩性，鲁棒性和可解释性。我们描述了一个通用检索增强的机器学习（REML）框架，其中包括许多现有模型作为特殊情况。 REML挑战信息检索惯例，为核心领域的新进步提供了机会，包括优化。 REML研究议程为新的信息访问研究奠定了基础，并为推进机器学习和人工智能的道路铺平了道路。
Although information access systems have long supported people in accomplishing a wide range of tasks, we propose broadening the scope of users of information access systems to include task-driven machines, such as machine learning models. In this way, the core principles of indexing, representation, retrieval, and ranking can be applied and extended to substantially improve model generalization, scalability, robustness, and interpretability. We describe a generic retrieval-enhanced machine learning (REML) framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. The REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
