Customer-obsessed science
Research areas
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November 20, 20254 min readA new evaluation pipeline called FiSCo uncovers hidden biases and offers an assessment framework that evolves alongside language models.
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September 2, 20253 min read
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Featured news
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ACM MMSports 20232023Sports highlights are an important form of media for fans worldwide, as they provide short videos that capture key moments from games, often accompanied by the original commentaries of the game’s announcers. However, traditional forms of presenting sports highlights have limitations in conveying the complexity and nuance of the game. In recent years, the use of Large Language Models (LLMs) for natural language
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WACV 20232023Style transfer for human face has been widely researched in recent years. Majority of the existing approaches work in 2D image domain and have 3D inconsistency issue when applied on different viewpoints of the same face. In this paper, we tackle the problem of 3D face style transfer which aims at generating stylized novel views of a 3D human face with multi-view consistency. We propose to use a neural radiance
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NeurIPS 20232023We introduce Alexa Arena, a user-centric simulation platform to facilitate research in building assistive conversational embodied agents. Alexa Arena features multi-room layouts and an abundance of interactable objects. With user-friendly graphics and control mechanisms, the platform supports the development of gamified robotic tasks readily accessible to general human users, allowing high-efficiency data
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EMNLP 20232023The growing popularity of conversational AI agents such as Alexa, Google Assistant, and Siri relies on accurate spoken-language comprehension. The query reformulation (QR) method, which reformulates defective user queries, has been broadly adopted to mitigate the challenges posed by understanding the user’s intent from an imperfect spoken recognition result. However, due to the scarcity of non- English
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EMNLP 20232023Contextual query rewriting (CQR) is a crucial component in Conversational AI agents, leveraging the contextual information from previous user-agent conversations to improve the comprehension of current user intent. However, traditional CQR methods often concentrate on supervised fine-tuning only, neglecting the opportunities to learn from user feedback to align with user preferences. Inspired by recent
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