Customer-obsessed science
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June 13, 2024The fight against hallucination in retrieval-augmented-generation models starts with a method for accurately assessing it.
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June 13, 2024As in other areas of AI, generative models and foundation models — such as vision-language models — are a hot topic.
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June 07, 2024Although work involving large language models predominates, classical and more-general techniques remain well represented.
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June 16 - 21, 2024
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June 17 - 21, 2024
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July 14 - 18, 2024
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February 15, 2024
In addition to its practical implications, recent work on “meaning representations” could shed light on some old philosophical questions.
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April 16, 2024First model to work across a wide range of products uses a second U-Net encoder to capture fine-grained product details.
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March 18, 2024Tokenizing time series data and treating it like a language enables a model whose zero-shot performance matches or exceeds that of purpose-built models.
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February 20, 2024Generative AI supports the creation, at scale, of complex, realistic driving scenarios that can be directed to specific locations and environments.
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January 17, 2024Representing facts using knowledge triplets rather than natural language enables finer-grained judgments.
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Resource, Conservation and Recycling2024The Circular Economy (CE) has been proposed as a strategy to promote the efficient use of resources, maximizing the benefits derived from materials and products through value recovery strategies, and minimizing waste generation. However, ambiguity remains in defining what makes a product circular and its characteristics when adapting the CE concept for application at the product level. More clarity about the
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FORC 20242024We study the problem of collecting a cohort or set that is balanced with respect to sensitive groups when group membership is unavailable or prohibited from use at deployment time. Specifically, our deployment-time collection mechanism does not reveal significantly more about the group membership of any individual sample than can be ascertained from base rates alone. To do this, we study a learner that
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2024How do we transfer the relevant knowledge from ever larger foundation models into small, task-specific downstream models that can run at much lower costs? Standard transfer learning using pre-trained weights as the initialization transfers limited information and commits us to often massive pre-trained architectures. This procedure also precludes combining multiple pre-trained models that learn complementary
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2024Deep learning-based Natural Language Processing (NLP) models are vulnerable to adversarial attacks, where small perturbations can cause a model to misclassify. Adversarial Training (AT) is often used to increase model robustness. However, we have discovered an intriguing phenomenon: deliberately or accidentally miscalibrating models masks gradients in a way that interferes with adversarial attack search
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ACL Findings 20242024Large language models (LLMs) have demonstrated remarkable open-domain capabilities. LLMs tailored for a domain are typically trained entirely on domain corpus to excel at handling domain-specific tasks. In this work, we explore an alternative strategy of continual pre-training as a means to develop domain-specific LLMs over an existing open-domain LLM. We introduce FinPythia-6.9B, developed through domain-adaptive
Resources
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Supporting research at academic institutions and non-profit organizations in areas that align with our mission to advance customer-obsessed science.