60 citations · 151 across the 14 of their papers we have counts for
5 papers · 1 filter
ConvXAI: Delivering Heterogeneous AI Explanations via Conversations to Support Human-AI Scientific Writing
Hua Shen, Chieh-Yang Huang, Tongshuang Wu +1
Despite a surge collection of XAI methods, users still struggle to obtain required AI explanations. Previous research suggests chatbots as dynamic solutions, but the effective desi…
Parachute: Evaluating Interactive Human-LM Co-writing Systems
Hua Shen, Tongshuang Wu
A surge of advances in language models (LMs) has led to significant interest in using LMs to build co-writing systems, in which humans and LMs interactively contribute to a shared…
ScatterShot: Interactive In-context Example Curation for Text Transformation
Tongshuang Wu, Hua Shen, Daniel S. Weld +2
The in-context learning capabilities of LLMs like GPT-3 allow annotators to customize an LLM to their specific tasks with a small number of examples. However, users tend to include…
Too Slow to Be Useful? On Incorporating Humans in the Loop of Smart Speakers
Shih-Hong Huang, Chieh-Yang Huang, Yuxin Deng +3
Real-time crowd-powered systems, such as Chorus/Evorus, VizWiz, and Apparition, have shown how incorporating humans into automated systems could supplement where the automatic solu…
How Useful Are the Machine-Generated Interpretations to General Users? A Human Evaluation on Guessing the Incorrectly Predicted Labels
Hua Shen, Ting-Hao Kenneth Huang
Explaining to users why automated systems make certain mistakes is important and challenging. Researchers have proposed ways to automatically produce interpretations for deep neura…