Related papers: Retrieval-Enhanced Machine Learning

Retrieval-Enhanced Machine Learning

URL: http://arxiv.org/abs/2205.01230v1
Date: Mon, 2 May 2022 21:42:45 GMT
Title: Retrieval-Enhanced Machine Learning
Authors: Hamed Zamani and Fernando Diaz and Mostafa Dehghani and Donald Metzler and Michael Bendersky
Abstract summary: We describe a generic retrieval-enhanced machine learning 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. 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.
Score: 110.5237983180089
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

Related papers

Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
arXiv Detail & Related papers (2024-07-17T20:01:21Z)
A Survey of Serverless Machine Learning Model Inference [0.0]
Generative AI, Computer Vision, and Natural Language Processing have led to an increased integration of AI models into various products. This survey aims to summarize and categorize the emerging challenges and optimization opportunities for large-scale deep learning serving systems.
arXiv Detail & Related papers (2023-11-22T18:46:05Z)
RRAML: Reinforced Retrieval Augmented Machine Learning [10.94680155282906]
We propose a novel framework called Reinforced Retrieval Augmented Machine Learning (RRAML) RRAML integrates the reasoning capabilities of large language models with supporting information retrieved by a purpose-built retriever from a vast user-provided database. We believe that the research agenda outlined in this paper has the potential to profoundly impact the field of AI.
arXiv Detail & Related papers (2023-07-24T13:51:19Z)
Synergistic Interplay between Search and Large Language Models for Information Retrieval [141.18083677333848]
InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections. InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods.
arXiv Detail & Related papers (2023-05-12T11:58:15Z)
On-device Training: A First Overview on Existing Systems [6.551096686706628]
Efforts have been made to deploy some models on resource-constrained devices as well. This work targets to summarize and analyze state-of-the-art systems research that allows such on-device model training capabilities.
arXiv Detail & Related papers (2022-12-01T19:22:29Z)
A Survey of Machine Unlearning [56.017968863854186]
Recent regulations now require that, on request, private information about a user must be removed from computer systems. ML models often remember' the old data. Recent works on machine unlearning have not been able to completely solve the problem.
arXiv Detail & Related papers (2022-09-06T08:51:53Z)
Towards Machine Learning for Placement and Routing in Chip Design: a Methodological Overview [72.79089075263985]
Placement and routing are two indispensable and challenging (NP-hard) tasks in modern chip design flows. Machine learning has shown promising prospects by its data-driven nature, which can be of less reliance on knowledge and priors.
arXiv Detail & Related papers (2022-02-28T06:28:44Z)
A Survey on Semi-parametric Machine Learning Technique for Time Series Forecasting [4.9341230675162215]
Grey Machine Learning (GML) is capable of handling large datasets as well as small datasets for time series forecasting likely outcomes. This survey presents a comprehensive overview of the existing semi-parametric machine learning techniques for time series forecasting.
arXiv Detail & Related papers (2021-04-02T03:26:20Z)
A Survey on Large-scale Machine Learning [67.6997613600942]
Machine learning can provide deep insights into data, allowing machines to make high-quality predictions. Most sophisticated machine learning approaches suffer from huge time costs when operating on large-scale data. Large-scale Machine Learning aims to learn patterns from big data with comparable performance efficiently.
arXiv Detail & Related papers (2020-08-10T06:07:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.