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pro vyhledávání: '"Weisz, Justin D"'
Design fictions allow us to prototype the future. They enable us to interrogate emerging or non-existent technologies and examine their implications. We present three design fictions that probe the potential consequences of operationalizing a mutual
Externí odkaz:
http://arxiv.org/abs/2406.11946
Autor:
Do, Hyo Jin, Ostrand, Rachel, Weisz, Justin D., Dugan, Casey, Sattigeri, Prasanna, Wei, Dennis, Murugesan, Keerthiram, Geyer, Werner
While humans increasingly rely on large language models (LLMs), they are susceptible to generating inaccurate or false information, also known as "hallucinations". Technical advancements have been made in algorithms that detect hallucinated content b
Externí odkaz:
http://arxiv.org/abs/2405.20434
Autor:
Weisz, Justin D., He, Jessica, Muller, Michael, Hoefer, Gabriela, Miles, Rachel, Geyer, Werner
Generative AI applications present unique design challenges. As generative AI technologies are increasingly being incorporated into mainstream applications, there is an urgent need for guidance on how to design user experiences that foster effective
Externí odkaz:
http://arxiv.org/abs/2401.14484
Large language models (LLMs) have recently been applied in software engineering to perform tasks such as translating code between programming languages, generating code from natural language, and autocompleting code as it is being written. When used
Externí odkaz:
http://arxiv.org/abs/2302.07080
The Programmer's Assistant is an experimental prototype software development environment that integrates a chatbot with a code editor. Conversational capability was achieved by using an existing code-fluent Large Language Model and providing it with
Externí odkaz:
http://arxiv.org/abs/2301.10016
Generative AI technologies are growing in power, utility, and use. As generative technologies are being incorporated into mainstream applications, there is a need for guidance on how to design those applications to foster productive and safe use. Bas
Externí odkaz:
http://arxiv.org/abs/2301.05578
Autor:
Weisz, Justin D., Muller, Michael, Ross, Steven I., Martinez, Fernando, Houde, Stephanie, Agarwal, Mayank, Talamadupula, Kartik, Richards, John T.
Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from code, and auto-completing methods. Yet, state-of-the-art models often pr
Externí odkaz:
http://arxiv.org/abs/2202.07682
Autor:
Sun, Jiao, Liao, Q. Vera, Muller, Michael, Agarwal, Mayank, Houde, Stephanie, Talamadupula, Kartik, Weisz, Justin D.
What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative models. Less attention has been paid to generative models that produce a
Externí odkaz:
http://arxiv.org/abs/2202.04903
Autor:
Agarwal, Mayank, Talamadupula, Kartik, Martinez, Fernando, Houde, Stephanie, Muller, Michael, Richards, John, Ross, Steven I, Weisz, Justin D.
Translating source code from one programming language to another is a critical, time-consuming task in modernizing legacy applications and codebases. Recent work in this space has drawn inspiration from the software naturalness hypothesis by applying
Externí odkaz:
http://arxiv.org/abs/2110.05423
Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and generating candidate models. A critical, final step of AutoML is human selection of a final model f
Externí odkaz:
http://arxiv.org/abs/2104.04375