Hai Dang

schoolUniversity of Bayreuthbar_chart#2373 Global Rank

Total Papers

HCI Rank

#2373

Research Areas

Years Active

Recent Publications

Authoring LLM-Based Assistance for Real-World Contexts and Tasks

Advances in AI hold the possibility of assisting users with highly varied and individual needs, but the breadth of assistance that these systems could provide creates a challenge for how users specify their goals to the system. To support the authoring of AI assistance for real-world tasks, we propose the concept of C…

CHI '251st AuthorAI-Assisted Creative Writing +1

CorpusStudio: Surfacing Emergent Patterns In A Corpus Of Prior Work While Writing

Many communities, including the scientific community, develop implicit writing norms. Understanding them is crucial for effective communication with that community. Writers gradually develop an implicit understanding of norms by reading papers and receiving feedback on their writing. However, it is difficult to both e…

CHI '231st AuthorHuman-LLM Collaboration +1

Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting

We propose a conceptual perspective on prompts for Large Language Models (LLMs) that distinguishes between (1) diegetic prompts (part of the narrative, e.g. “Once upon a time, I saw a fox...”), and (2) non-diegetic prompts (external, e.g. “Write about the adventures of the fox.”). With this lens, we study how 129 crow…

UIST '221st AuthorHuman-LLM Collaboration +1

Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summaries.

We propose a text editor to help users plan, structure and reflect on their writing process. It provides continuously updated paragraph-wise summaries as margin annotations, using automatic text summarization. Summary levels range from full text, to selected (central) sentences, down to a collection of keywords. To un…

CHI '221st AuthorGenerative AI (Text, Image, Music, Video) +2

GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Information

We investigate how multiple sliders with and without feedforward visualizations influence users' control of generative models. In an online study (N=138), we collected a dataset of people interacting with a generative adversarial network (StyleGAN2) in an image reconstruction task. We found that more control dimension…

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