activity
20222024
most cited"What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models

115 citations · 243 across the 8 of their papers we have counts for

collaborators

8 papers

cs.HC2024★ 47 cited

Improving Steering and Verification in AI-Assisted Data Analysis with Interactive Task Decomposition

Majeed Kazemitabaar, Jack Williams, Ian Drosos +4

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…

cs.HC2024★ 28 cited

"It's like a rubber duck that talks back": Understanding Generative AI-Assisted Data Analysis Workflows through a Participatory Prompting Study

Ian Drosos, Advait Sarkar, Xiaotong Xu +3

Generative AI tools can help users with many tasks. One such task is data analysis, which is notoriously challenging for non-expert end-users due to its expertise requirements, and…

cs.PL2024

Solving Data-centric Tasks using Large Language Models

Shraddha Barke, Christian Poelitz, Carina Suzana Negreanu +10

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…

cs.HC2023★ 3 cited

Participatory prompting: a user-centric research method for eliciting AI assistance opportunities in knowledge workflows

Advait Sarkar, Ian Drosos, Rob Deline +5

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 re…

cs.HC2023★ 3 cited

Co-audit: tools to help humans double-check AI-generated content

Andrew D. Gordon, Carina Negreanu, José Cambronero +9

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, tab…

cs.HC2023★ 115 cited

"What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models

Michael Xieyang Liu, Advait Sarkar, Carina Negreanu +4

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 c…

cs.HC2022★ 47 cited

What is it like to program with artificial intelligence?

Advait Sarkar, Andrew D. Gordon, Carina Negreanu +3

Large language models, such as OpenAI's codex and Deepmind's AlphaCode, can generate code to solve a variety of problems expressed in natural language. This technology has already…

cs.CL2022

Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model

Carina Negreanu, Alperen Karaoglu, Jack Williams +4

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 populati…