Summary of A Statistical Framework For Data-dependent Retrieval-augmented Models, by Soumya Basu et al.
A Statistical Framework for Data-dependent Retrieval-Augmented Models
by Soumya Basu, Ankit Singh Rawat, Manzil Zaheer
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 research proposes a statistical framework to study retrieval-augmented machine learning (ML) models, which combine input instances with additional relevant information to enhance final predictions. The framework consists of two components: a retriever that identifies relevant information from a large corpus using a data-dependent metric, and a predictor that consumes the input instances along with the retrieved information to make predictions. The authors present a principled method for end-to-end training of both components and establish excess risk bounds for retrieval-augmented models while delineating the contributions of both retriever and predictor towards model performance. This study is relevant to open domain question answering, where retrieval augmentation is important. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Retrieval-augmented machine learning (ML) systems are becoming more popular because they can help predict better by combining input information with additional relevant details. However, researchers don’t fully understand how these models work or how to train them effectively. This study proposes a new way to analyze and train retrieval-augmented ML models. It includes two parts: finding the most important information from a large collection of data and using that information to make predictions. The authors also provide guidelines for training both parts together and explain why their approach works well. They tested their method on open domain question answering, which is an area where getting extra information can be very helpful. |
Keywords
» Artificial intelligence » Machine learning » Question answering
