2022 RetrievalEnhancedMachineLearnin
- (Zamani et al., 2022) ⇒ Hamed Zamani, Fernando Diaz, Mostafa Dehghani, Donald Metzler, and Michael Bendersky. (2022). “Retrieval-Enhanced Machine Learning.” In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. doi:10.1145/3477495.3531722
Subject Headings: RAG Algorithm.
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Abstract
Although information access systems have long supportedpeople 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.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022 RetrievalEnhancedMachineLearnin | Michael Bendersky Donald Metzler Mostafa Dehghani Hamed Zamani Fernando Diaz | Retrieval-enhanced Machine Learning | 10.1145/3477495.3531722 | 2022 |