Search Results for author: John Joon Young Chung

Found 7 papers, 3 papers with code

One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generations

no code implementations9 May 2024 Yoonjoo Lee, Kihoon Son, Tae Soo Kim, Jisu Kim, John Joon Young Chung, Eytan Adar, Juho Kim

Based on these findings, we present design implications that, instead of regarding LLM output inconsistencies as a drawback, we can reveal the potential inconsistencies to transparently indicate the limitations of these models and promote critical LLM usage.

Authors' Values and Attitudes Towards AI-bridged Scalable Personalization of Creative Language Arts

no code implementations1 Mar 2024 Taewook Kim, Hyomin Han, Eytan Adar, Matthew Kay, John Joon Young Chung

Generative AI has the potential to create a new form of interactive media: AI-bridged creative language arts (CLA), which bridge the author and audience by personalizing the author's vision to the audience's context and taste at scale.

PromptPaint: Steering Text-to-Image Generation Through Paint Medium-like Interactions

1 code implementation9 Aug 2023 John Joon Young Chung, Eytan Adar

Just as we iteratively tune colors through layered placements of paint on a physical canvas, PromptPaint similarly allows users to apply different prompts to different canvas areas and times of the generative process.

Text-to-Image Generation

Neglected Free Lunch -- Learning Image Classifiers Using Annotation Byproducts

3 code implementations30 Mar 2023 Dongyoon Han, Junsuk Choe, Seonghyeok Chun, John Joon Young Chung, Minsuk Chang, Sangdoo Yun, Jean Y. Song, Seong Joon Oh

We refer to the new paradigm of training models with annotation byproducts as learning using annotation byproducts (LUAB).

Time Series

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