25 citations · 27 across the 7 of their papers we have counts for
7 papers
ScholaWrite: A Dataset of End-to-End Scholarly Writing Process
Khanh Chi Le, Linghe Wang, Minhwa Lee +3
Writing is a cognitively demanding activity that requires constant decision-making, heavy reliance on working memory, and frequent shifts between tasks of different goals. To build…
Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants
Minhwa Lee, Zae Myung Kim, Vivek Khetan +1
Large Language Models (LLMs) have assisted humans in several writing tasks, including text revision and story generation. However, their effectiveness in supporting domain-specific…
LocalTweets to LocalHealth: A Mental Health Surveillance Framework Based on Twitter Data
Vijeta Deshpande, Minhwa Lee, Zonghai Yao +3
Prior research on Twitter (now X) data has provided positive evidence of its utility in developing supplementary health surveillance systems. In this study, we present a new framew…
Under the Surface: Tracking the Artifactuality of LLM-Generated Data
Debarati Das, Karin De Langis, Anna Martin-Boyle +14
This work delves into the expanding role of large language models (LLMs) in generating artificial data. LLMs are increasingly employed to create a variety of outputs, including ann…
How Far Can We Extract Diverse Perspectives from Large Language Models?
Shirley Anugrah Hayati, Minhwa Lee, Dheeraj Rajagopal +1
Collecting diverse human opinions is costly and challenging. This leads to a recent trend in exploiting large language models (LLMs) for generating diverse data for potential scala…
Benchmarking Cognitive Biases in Large Language Models as Evaluators
Ryan Koo, Minhwa Lee, Vipul Raheja +3
Large Language Models are cognitively biased judges. Large Language Models (LLMs) have recently been shown to be effective as automatic evaluators with simple prompting and in-cont…
Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information
Sunjae Kwon, Rishabh Garodia, Minhwa Lee +2
Visual Word Sense Disambiguation (VWSD) is a task to find the image that most accurately depicts the correct sense of the target word for the given context. Previously, image-text…