Customer-obsessed science


Research areas
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July 18, 2025Novel graph-based, adversarial, agentic method for generating training examples helps identify — and mitigate — "overrefusal".
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State-of-the-art performance has been achieved in recent years on tasks such as search, recommendation and classification using Visuo-Lingual Multi-Modal models. While the pre-trained Vision-Language models like Contrastive Language-Image Pre-training (CLIP) have achieved promising zero-shot performance on several generalized tasks by learning vision-language concepts in a common space, the natural hierarchical
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2024Knowledge graphs (KGs) complement Large Language Models (LLMs) by providing reliable, structured, domain-specific, and up-to-date external knowledge. However, KGs and LLMs are often developed separately and must be integrated after training. We introduce Tree-of-Traversals, a novel zero-shot reasoning algorithm that enables augmentation of black-box LLMs with one or more KGs. The algorithm equips a LLM
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A complementary item is an item that pairs well with another item when consumed together. In the context of e-commerce, providing recommendations for complementary items is essential for both customers and stores. Current models for suggesting complementary items often rely heavily on user behavior data, such as co-purchase relationships. However, just because two items are frequently bought together does
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2024In this work, we introduce Context-Aware MultiModal Learner (CaMML), for tuning large multimodal models (LMMs). CaMML, a lightweight module, is crafted to seamlessly integrate multimodal contextual samples into large models, thereby empowering the model to derive knowledge from analogous, domain-specific, up-to-date information and make grounded inferences. Importantly, CaMML is highly scalable and can
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IEEE Robotics and Automation Letters2024Home robots intend to make their users lives easier. Our work moves toward more helpful home robots by enabling them to inform their users of dangerous or unsanitary anomalies in the home. Some examples of these anomalies include the user leaving their milk out, forgetting to turn off the stove, or leaving poison accessible to children. To enable home robots with these abilities, we have created a new dataset
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