most citedTowards Transparent, Reusable, and Customizable Data Science in Computational Notebooks

5 citations · 12 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20244 cited

MEGAnno+: A Human-LLM Collaborative Annotation System

Hannah Kim, Kushan Mitra, Rafael Li Chen +2

Large language models (LLMs) can label data faster and cheaper than humans for various NLP tasks. Despite their prowess, LLMs may fall short in understanding of complex, sociocultu…

cs.HC2024

Knowledge Acquisition and Integration with Expert-in-the-loop

Sajjadur Rahman, Frederick Choi, Hannah Kim +2

Constructing and serving knowledge graphs (KGs) is an iterative and human-centered process involving on-demand programming and analysis. In this paper, we present Kyurem, a program…

cs.CL20242 cited

Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions

Pouya Pezeshkpour, Eser Kandogan, Nikita Bhutani +3

Remarkable performance of large language models (LLMs) in a variety of tasks brings forth many opportunities as well as challenges of utilizing them in production settings. Towards…

cs.HC20235 cited

Towards Transparent, Reusable, and Customizable Data Science in Computational Notebooks

Frederick Choi, Sajjadur Rahman, Hannah Kim +1

Data science workflows are human-centered processes involving on-demand programming and analysis. While programmable and interactive interfaces such as widgets embedded within comp…

cs.DB20231 cited

Towards Multifaceted Human-Centered AI

Sajjadur Rahman, Hannah Kim, Dan Zhang +2

Human-centered AI workflows involve stakeholders with multiple roles interacting with each other and automated agents to accomplish diverse tasks. In this paper, we call for a holi…