In collaboration with Carnegie Mellon University, Georgia Institute of Technology
AuthorsMary Beth Kery, Donghao Ren, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Kayur Patel
We aim to increase the flexibility at which a data worker can choose the right tool for the job, regardless of whether the tool is a code library or an interactive graphical user interface (GUI). To achieve this flexibility, we extend computational notebooks with a new API mage, which supports tools that can represent themselves as both code and GUI as needed. We discuss the design of mage as well as design opportunities in the space of flexible code/GUI tools for data work. To understand tooling needs, we conduct a study with nine professional practitioners and elicit their feedback on mage and potential areas for flexible code/GUI tooling. We then implement six client tools for mage that illustrate the main themes of our study findings. Finally, we discuss open challenges in providing flexible code/GUI interactions for data workers.
July 25, 2025research area Speech and Natural Language Processing, research area Tools, Platforms, Frameworksconference ACL
Pairwise preferences over model responses are widely collected to evaluate and provide feedback to large language models (LLMs). Given two alternative model responses to the same input, a human or AI annotator selects the ``better'' response. Such data can provide a feedback signal in domains where traditional hard-coded metrics are difficult to obtain (e.g. quality of a chat interactions), thereby helping measure model progress or model...
August 3, 2024research area Human-Computer Interaction, research area Tools, Platforms, Frameworksconference IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
This paper was accepted at IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) 2024.
Programmers frequently engage with machine learning tutorials in computational notebooks and have been adopting code generation technologies based on large language models (LLMs). However, they encounter difficulties in understanding and working with code produced by LLMs. To mitigate these challenges, we introduce a novel workflow into...
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