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pro vyhledávání: '"Gordon Andrew"'
Autor:
Roemmele, Melissa, Gordon, Andrew S.
LLMs can now perform a variety of complex writing tasks. They also excel in answering questions pertaining to natural language inference and commonsense reasoning. Composing these questions is itself a skilled writing task, so in this paper we consid
Externí odkaz:
http://arxiv.org/abs/2410.14897
What if end users could own the software development lifecycle from conception to deployment using only requirements expressed in language, images, video or audio? We explore this idea, building on the capabilities that generative Artificial Intellig
Externí odkaz:
http://arxiv.org/abs/2405.13708
Video tutorials are a popular medium for informal and formal learning. However, when learners attempt to view and follow along with these tutorials, they encounter what we call gaps, that is, issues that can prevent learning. We examine the gaps enco
Externí odkaz:
http://arxiv.org/abs/2404.07114
Autor:
Barke, Shraddha, Poelitz, Christian, Negreanu, Carina Suzana, Zorn, Benjamin, Cambronero, José, Gordon, Andrew D., Le, Vu, Nouri, Elnaz, Polikarpova, Nadia, Sarkar, Advait, Slininger, Brian, Toronto, Neil, Williams, Jack
Large language models (LLMs) are rapidly replacing help forums like StackOverflow, and are especially helpful for non-professional programmers and end users. These users are often interested in data-centric tasks, such as spreadsheet manipulation and
Externí odkaz:
http://arxiv.org/abs/2402.11734
Autor:
Sarkar, Advait, Drosos, Ian, Deline, Rob, Gordon, Andrew D., Negreanu, Carina, Rintel, Sean, Williams, Jack, Zorn, Benjamin
Publikováno v:
Proceedings of the 34th Annual Conference of the Psychology of Programming Interest Group (PPIG 2023)
Generative AI, such as image generation models and large language models, stands to provide tremendous value to end-user programmers in creative and knowledge workflows. Current research methods struggle to engage end-users in a realistic conversatio
Externí odkaz:
http://arxiv.org/abs/2312.16633