most citedModel Sketching: Centering Concepts in Early-Stage Machine Learning Model Design

27 citations · 35 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CL20233 cited

Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks

Yuxuan Lu, Bingsheng Yao, Shao Zhang +5

Large Language Models (LLMs) have demonstrated considerable advances, and several claims have been made about their exceeding human performance. However, in real-world tasks, domai…

cs.CY20232 cited

Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education

Mei Tan, Hansol Lee, Dakuo Wang +1

Despite the promises of ML in education, its adoption in the classroom has surfaced numerous issues regarding fairness, accountability, and transparency, as well as concerns about…

cs.CL2023

'Don't Get Too Technical with Me': A Discourse Structure-Based Framework for Science Journalism

Ronald Cardenas, Bingsheng Yao, Dakuo Wang +1

Science journalism refers to the task of reporting technical findings of a scientific paper as a less technical news article to the general public audience. We aim to design an aut…

cs.CL2023

PaniniQA: Enhancing Patient Education Through Interactive Question Answering

Pengshan Cai, Zonghai Yao, Fei Liu +9

Patient portal allows discharged patients to access their personalized discharge instructions in electronic health records (EHRs). However, many patients have difficulty understand…

cs.CL20231 cited

Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children's Fairy Tales

Paulina Toro Isaza, Guangxuan Xu, Akintoye Oloko +3

Social biases and stereotypes are embedded in our culture in part through their presence in our stories, as evidenced by the rich history of humanities and social science literatur…

cs.CL2023

Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations

Bingsheng Yao, Prithviraj Sen, Lucian Popa +2

Human-annotated labels and explanations are critical for training explainable NLP models. However, unlike human-annotated labels whose quality is easier to calibrate (e.g., with a…