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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EMNLP 20232023Modern ML systems ingest data aggregated from diverse sources, such as synthetic, human-annotated, and live customer traffic. Understanding which examples are important to the performance of a learning algorithm is crucial for efficient model training. Recently, a growing body of literature has given rise to various “influence scores,” which use training artifacts such as model confidence or checkpointed
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ACMMM 20232023Socially intelligent systems such as home robots should be able to perceive emotions and social behaviors. Affect recognition datasets have limited labeled data, and existing large unlabeled datasets, e.g., VoxCeleb2, suitable for pre-training, mostly contain neutral expressions, limiting their application to affective downstream tasks. We introduce a novel Semi-supervised Affective Adaptation framework
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EMNLP 20232023Most e-commerce search engines use customer behavior signals to augment lexical matching and improve search relevance. Many ecommerce companies like Amazon, Alibaba, Ebay etc. operate in multiple countries with country specific stores. However, customer behavior data is sparse in newer stores. To compensate for sparsity of behavioral data in low traffic stores, search engines often use crosslisted products
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EMNLP 20232023In the Multi-document summarization (MDS) task, a summary is produced for a given set of documents. A recent line of research introduced the concept of damaging documents, denoting documents that should not be exposed to readers due to various reasons. In the presence of damaging documents, a summarizer is ideally expected to exclude damaging content in its output. Existing metrics evaluate a summary based
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EMNLP 20232023The common practice for assessing automatic evaluation metrics is to measure the correlation between their induced system rankings and those obtained by reliable human evaluation, where a higher correlation indicates a better metric. Yet, an intricate setting arises when an NLP task is evaluated by multiple Quality Criteria (QCs), like for text summarization where prominent criteria include relevance, consistency
Resources
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We look for talent from around the world for applied scientists, data scientists, economists, research scientists, scholars, academics, PhDs, and interns.
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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.