Customer-obsessed science


Research areas
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June 25, 2025With large datasets, directly generating data ID codes from query embeddings is much more efficient than performing pairwise comparisons between queries and candidate responses.
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Large language models (LLMs) exhibit excellent ability to understand human languages, but do they also understand their own language that appears gibberish to us? In this work we delve into this question, aiming to uncover the mechanisms underlying such behavior in LLMs. We employ the Greedy Coordinate Gradient optimizer to craft prompts that compel LLMs to generate coherent responses from seemingly nonsensical
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2024Large models training is plagued by the intense compute cost and limited hardware memory. A practical solution is low-precision representation but is troubled by loss in numerical accuracy and unstable training rendering the model less useful. We argue that low-precision floating points can perform well provided the error is properly compensated at the critical locations in the training process. We propose
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ACL 2024 Workshop on NLP for Conversational AI2024Continued improvement of conversational assistants in knowledge-rich domains like E-Commerce requires large volumes of realistic high-quality conversation data to power increasingly sophisticated LLM chatbots, dialogue managers, response rankers, and recommenders. The problem is worse for multi-modal interactions in realistic conversational product search and recommendation. Here, an artificial sales agent
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E-commerce stores typically test changes to ranking algorithms through rigorous A/B testing which requires a change to satisfy some predefined success criteria on multiple metrics. This problem of simultaneously optimization of multiple metrics is multi-objective-optimization (MOO). A common method for MOO is to choose a set of weights to scalarize the multiple metrics into one ranking objective. However
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Winter Simulation Conference 20242024Organizations today are integrating technologies such as cloud computing, and digital twin in their manufacturing and logistical processes. In a capital-intensive logistics industry, Discrete event simulation (DES) plays a crucial role in distribution center design, automation system performance analysis, optimization and operational planning. Developing and deploying DES models demands proficiency in various
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