Customer-obsessed science


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May 29, 2025In both black-box stress testing and red-team exercises, Nova Premier comes out on top.
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NAACL 2025 Workshop on TrustNLP2025Large Language Models (LLMs) have demonstrated excellent capabilities in Question Answering (QA) tasks, yet their ability to identify and address ambiguous questions remains underdeveloped. Ambiguities in user queries often lead to inaccurate or misleading answers, undermining user trust in these systems. Despite prior attempts using prompt-based methods, performance has largely been equivalent to random
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ACL 20252025We present MegaBeam-Mistral-7B1, a language model that supports 512K-token context length. Our work addresses practical limitations in long-context training, supporting real-world tasks such as compliance monitoring and verification. Evaluated on three long-context benchmarks, our 7B-parameter model demonstrates superior in-context learning performance on HELMET and robust retrieval and tracing capability
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ACL 20252025Accurate mapping of queries to product categories is crucial for efficient retrieval and ranking of relevant products in e-commerce search. Conventionally, such query classification models rely on supervised learning using historical user interactions, but their effectiveness diminishes in cold-start scenarios, where new categories or products lack sufficient training data. This results in poor query-to-category
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ACL 20252025Large Language Models (LLMs) enable natural language to SQL conversion, allowing users to query databases without SQL expertise. However, generating accurate, efficient queries is challenging due to ambiguous intent, domain knowledge requirements, and database constraints. Extensive reasoning improves SQL quality but increases computational costs and latency. We propose SQLGenie, a practical system for
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2025This paper presents an integrated robotic system designed for autonomous picking of targeted objects from cluttered and deformable shelves—a critical task in Amazon warehouse operations for processing customer orders. The system addresses common challenges in robotic picking including diverse object handling, densely packed storage, and dynamic inventories. However, shelf-picking introduces additional complexities
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