283 citations · 641 across the 9 of their papers we have counts for
9 papers
Current and Future Use of Large Language Models for Knowledge Work
Michelle Brachman, Amina El-Ashry, Casey Dugan +1
Large Language Models (LLMs) have introduced a paradigm shift in interaction with AI technology, enabling knowledge workers to complete tasks by specifying their desired outcome in…
Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions
Hyo Jin Do, Rachel Ostrand, Justin D. Weisz +5
While humans increasingly rely on large language models (LLMs), they are susceptible to generating inaccurate or false information, also known as "hallucinations". Technical advanc…
Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback
Shang-Ling Hsu, Raj Sanjay Shah, Prathik Senthil +4
Millions of users come to online peer counseling platforms to seek support. However, studies show that online peer support groups are not always as effective as expected, largely d…
Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness
Zahra Ashktorab, Benjamin Hoover, Mayank Agarwal +4
Mitigating algorithmic bias is a critical task in the development and deployment of machine learning models. While several toolkits exist to aid machine learning practitioners in a…
Increasing the Speed and Accuracy of Data LabelingThrough an AI Assisted Interface
Michael Desmond, Zahra Ashktorab, Michelle Brachman +8
Labeling data is an important step in the supervised machine learning lifecycle. It is a laborious human activity comprised of repeated decision making: the human labeler decides w…
Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks
April Yi Wang, Dakuo Wang, Jaimie Drozdal +6
Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code,…