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.
翻译:虽然信息访问系统长期以来支持人们完成范围广泛的任务,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,例如机器学习模式,从而可以应用和扩展指数化、代表性、检索和排名等核心原则,大大改进信息访问研究模式的通用性、可扩缩性、稳健性和可解释性。我们描述了一个通用的增强检索的机器学习框架,其中包括一些现有的模式,作为特殊案例。REML信息检索公约挑战信息检索公约,为核心领域的新进展提供了机会,包括优化。REML研究议程为信息访问研究的新风格奠定了基础,并为推进机器学习和人工智能铺平了道路。