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pro vyhledávání: '"Henley, A"'
Novice programmers are increasingly relying on Large Language Models (LLMs) to generate code for learning programming concepts. However, this interaction can lead to superficial engagement, giving learners an illusion of learning and hindering skill
Externí odkaz:
http://arxiv.org/abs/2410.08922
In this paper, we present Wandercode, a novel interaction design for recommender systems that recommend code locations to aid programmers in software development tasks. In particular, our design aims to improve upon prior designs by reducing informat
Externí odkaz:
http://arxiv.org/abs/2408.14589
Improving Steering and Verification in AI-Assisted Data Analysis with Interactive Task Decomposition
Autor:
Kazemitabaar, Majeed, Williams, Jack, Drosos, Ian, Grossman, Tovi, Henley, Austin, Negreanu, Carina, Sarkar, Advait
Publikováno v:
Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST 2024)
LLM-powered tools like ChatGPT Data Analysis, have the potential to help users tackle the challenging task of data analysis programming, which requires expertise in data processing, programming, and statistics. However, our formative study (n=15) unc
Externí odkaz:
http://arxiv.org/abs/2407.02651
Autor:
Singha, Ananya, Chopra, Bhavya, Khatry, Anirudh, Gulwani, Sumit, Henley, Austin Z., Le, Vu, Parnin, Chris, Singh, Mukul, Verbruggen, Gust
Automated insight generation is a common tactic for helping knowledge workers, such as data scientists, to quickly understand the potential value of new and unfamiliar data. Unfortunately, automated insights produced by large-language models can gene
Externí odkaz:
http://arxiv.org/abs/2405.01556
Autor:
Henley, Austin Z., Piorkowski, David
Without well-labeled ground truth data, machine learning-based systems would not be as ubiquitous as they are today, but these systems rely on substantial amounts of correctly labeled data. Unfortunately, crowdsourced labeling is time consuming and e
Externí odkaz:
http://arxiv.org/abs/2403.07762
Autor:
Kazemitabaar, Majeed, Ye, Runlong, Wang, Xiaoning, Henley, Austin Z., Denny, Paul, Craig, Michelle, Grossman, Tovi
Timely, personalized feedback is essential for students learning programming. LLM-powered tools like ChatGPT offer instant support, but reveal direct answers with code, which may hinder deep conceptual engagement. We developed CodeAid, an LLM-powered
Externí odkaz:
http://arxiv.org/abs/2401.11314
Autor:
Parnin, Chris, Soares, Gustavo, Pandita, Rahul, Gulwani, Sumit, Rich, Jessica, Henley, Austin Z.
A race is underway to embed advanced AI capabilities into products. These product copilots enable users to ask questions in natural language and receive relevant responses that are specific to the user's context. In fact, virtually every large techno
Externí odkaz:
http://arxiv.org/abs/2312.14231
Diffuse optical spectroscopy (DOS) techniques aim to characterize scattering media by examining their optical response to laser illumination. Time-domain DOS methods involve illuminating the medium with a laser pulse and using a fast photodetector to
Externí odkaz:
http://arxiv.org/abs/2310.20068
Autor:
Chopra, Bhavya, Singha, Ananya, Fariha, Anna, Gulwani, Sumit, Parnin, Chris, Tiwari, Ashish, Henley, Austin Z.
Large Language Models (LLMs) are being increasingly employed in data science for tasks like data preprocessing and analytics. However, data scientists encounter substantial obstacles when conversing with LLM-powered chatbots and acting on their sugge
Externí odkaz:
http://arxiv.org/abs/2310.16164