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pro vyhledávání: '"WILLIAMS, JACK"'
Effective prompting of generative AI is challenging for many users, particularly in expressing context for comprehension tasks such as explaining spreadsheet formulas, Python code, and text passages. Prompt middleware aims to address this barrier by
Externí odkaz:
http://arxiv.org/abs/2412.02357
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:
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
Autor:
Gordon, Andrew D., Negreanu, Carina, Cambronero, José, Chakravarthy, Rasika, Drosos, Ian, Fang, Hao, Mitra, Bhaskar, Richardson, Hannah, Sarkar, Advait, Simmons, Stephanie, Williams, Jack, Zorn, Ben
Users are increasingly being warned to check AI-generated content for correctness. Still, as LLMs (and other generative models) generate more complex output, such as summaries, tables, or code, it becomes harder for the user to audit or evaluate the
Externí odkaz:
http://arxiv.org/abs/2310.01297
Autor:
Liu, Michael Xieyang, Sarkar, Advait, Negreanu, Carina, Zorn, Ben, Williams, Jack, Toronto, Neil, Gordon, Andrew D.
Code-generating large language models translate natural language into code. However, only a small portion of the infinite space of naturalistic utterances is effective at guiding code generation. For non-expert end-user programmers, learning this is
Externí odkaz:
http://arxiv.org/abs/2304.06597
Autor:
Negreanu, Carina, Karaoglu, Alperen, Williams, Jack, Chen, Shuang, Fabian, Daniel, Gordon, Andrew, Lin, Chin-Yew
Row completion is the task of augmenting a given table of text and numbers with additional, relevant rows. The task divides into two steps: subject suggestion, the task of populating the main column; and gap filling, the task of populating the remain
Externí odkaz:
http://arxiv.org/abs/2204.07014