2 citations · 2 across the 5 of their papers we have counts for
6 papers
Protected group bias and stereotypes in Large Language Models
Hadas Kotek, David Q. Sun, Zidi Xiu +2
As modern Large Language Models (LLMs) shatter many state-of-the-art benchmarks in a variety of domains, this paper investigates their behavior in the domains of ethics and fairnes…
DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues
David Q. Sun, Artem Abzaliev, Hadas Kotek +3
Controversy is a reflection of our zeitgeist, and an important aspect to any discourse. The rise of large language models (LLMs) as conversational systems has increased public reli…
Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out
Zidi Xiu, Kai-Chen Cheng, David Q. Sun +7
With the growing popularity of intelligent assistants (IAs), evaluating IA quality becomes an increasingly active field of research. This paper identifies and quantifies the feedba…
Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution
David Q. Sun, Hadas Kotek, Christopher Klein +3
This paper develops and implements a scalable methodology for (a) estimating the noisiness of labels produced by a typical crowdsourcing semantic annotation task, and (b) reducing…
Generating Natural Questions from Images for Multimodal Assistants
Alkesh Patel, Akanksha Bindal, Hadas Kotek +2
Generating natural, diverse, and meaningful questions from images is an essential task for multimodal assistants as it confirms whether they have understood the object and scene in…
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant
Xi C. Chen, Adithya Sagar, Justine T. Kao +5
We describe a method for selecting relevant new training data for the LSTM-based domain selection component of our personal assistant system. Adding more annotated training data fo…