Customer-obsessed science
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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 10, 2024Automated reasoning and optimizations specific to CPU microarchitectures improve both performance and assurance of correct implementation.
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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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ASPLOS 20242024This paper introduces Fermihedral, a compiler framework focusing on discovering the optimal Fermion-to-qubit encod-ing for targeted Fermionic Hamiltonians. Fermion-to-qubit encoding is a crucial step in harnessing quantum comput-ing for efficient simulation of Fermionic quantum systems. Utilizing Pauli algebra, Fermihedral redefines complex constraints and objectives of Fermion-to-qubit encoding into a
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2024Existing dense retrieval systems utilize the same model architecture for encoding both the passages and the queries, even though queries are much shorter and simpler than passages. This leads to high latency of the query encoding, which is performed online and therefore might impact user experience. We show that combining a standard large passage encoder with a small efficient query encoder can provide
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2024We investigate the problem of synthesizing relevant visual imagery from generic long-form text, leveraging Large Language Models (LLMs) and Text-to-Image Models (TIMs). Current Text-to-Image models require short prompts that describe the image content and style explicitly. Unlike image prompts, generation of images from general long-form text requires the image synthesis system to derive the visual content
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2024In deep metric learning for visual recognition, the calibration of distance thresholds is crucial for achieving desired model performance in the true positive rates (TPR) or true negative rates (TNR). However, calibrating this threshold presents challenges in open-world scenarios, where the test classes can be entirely disjoint from those encountered during training. We define the problem of finding distance
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2024Single document news summarization has seen substantial progress on faithfulness in recent years, driven by research on the evaluation of factual consistency, or hallucinations. We ask whether these advances carry over to other text summarization domains. We propose a new evaluation benchmark on topic-focused dialogue summarization, generated by LLMs of varying sizes. We provide binary sentence-level human
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.