Summary of Retrieval-enhanced Machine Learning: Synthesis and Opportunities, by to Eun Kim et al.
Retrieval-Enhanced Machine Learning: Synthesis and Opportunities
by To Eun Kim, Alireza Salemi, Andrew Drozdov, Fernando Diaz, Hamed Zamani
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Retrieval-Enhanced Machine Learning (REML), a formal framework that combines language modeling with retrieval components. The authors argue that this paradigm can be extended to other machine learning domains like computer vision, time series prediction, and computational biology. They synthesize literature from various domains and provide a consistent notation system, which is currently missing in the field. Additionally, they bridge the gap between information retrieval research and contemporary REML studies by investigating each component of the framework. The goal is to equip researchers with a comprehensive framework, fostering interdisciplinary future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make machine learning models better by combining them with search technology. They think this can help many areas of study, like computer vision and biology, not just language processing. They’re missing some important information in the field, so they put together all the different ideas from different areas into one framework. They also connect their work to earlier research on searching for information. The goal is to give researchers a clear way to use this new approach, which will help many projects move forward. |
Keywords
» Artificial intelligence » Machine learning » Time series
