Customer-obsessed science
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September 30, 2024From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money.
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September 19, 2024“Agentic workflows” that use multiple, fine-tuned smaller LLMs — rather than one large one — can improve efficiency.
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September 16, 2024A position paper presented at ACL proposes a framework for more-accurate human evaluation of LLMs.
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September 29 - October 4, 2024
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October 21 - 25, 2024
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September 25, 2024
Now open until November 6, Amazon Research Awards will be seeking proposals in the following research areas: AI for Information Security, Automated Reasoning, AWS AI, AWS Cryptography, and Sustainability.
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In the field of Natural Language Processing (NLP), sentence pair classification is important in various real-world applications. Bi-encoders are commonly used to address these problems due to their low-latency requirements, and their ability to act as effective retrievers. However, bi-encoders often under-perform compared to cross-encoders by a significant margin. To address this gap, many Knowledge Distillation
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Federated Learning (FL) is a popular algorithm to train ma-chine learning models on user data constrained to edge devices (for example, mobile phones) due to privacy concerns. Typically, FL is trained with the assumption that no part of the user data can be egressed from the edge. However, in many production settings, specific data-modalities/meta-data are limited to be on device while others are not. For
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AISTATS 20242024Multi-objective optimization (MOO) aims to optimize multiple, possibly conflicting objectives with widespread applications. We introduce a novel interacting particle method for MOO inspired by molecular dynamics simulations. Our approach combines overdamped Langevin and birth-death dynamics, incorporating a “dominance potential” to steer particles toward global Pareto optimality. In contrast to previous
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2024Large Language models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities, where a LLM makes predictions for a given test input together with a few input-output pairs (demonstrations). Nevertheless, the inclusion of demonstrations leads to a quadratic increase in the computational overhead of the self-attention mechanism. Existing solutions attempt to distill lengthy demonstrations
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The Web Conference 2024, 2023 Conference on Digital Experimentation @ MIT (CODE@MIT)2024Adaptive experimental design (AED) methods are increasingly be-ing used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing methods. However, the behavior and guarantees of such methods are not well-understood beyond idealized stationary settings. This paper shares lessons learned regarding the challenges of naively using AED systems in
Resources
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We collaborate with leading academic organizations to drive innovation and to ensure that research is creating solutions whose benefits are shared broadly.
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Learn more about the awards and recognitions that Amazon researches from around the world have been honored with during their tenure.