165 citations · 420 across the 25 of their papers we have counts for
7 papers · 1 filter
Decisions that Explain Themselves: A User-Centric Deep Reinforcement Learning Explanation System
Xiaoran Wu, Zihan Yan, Chongjie Zhang +1
With deep reinforcement learning (RL) systems like autonomous driving being wildly deployed but remaining largely opaque, developers frequently use explainable RL (XRL) tools to be…
Capabilities for Better ML Engineering
Chenyang Yang, Rachel Brower-Sinning, Grace A. Lewis +2
In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation prefere…
Towards Natural Language-Based Visualization Authoring
Yun Wang, Zhitao Hou, Leixian Shen +5
A key challenge to visualization authoring is the process of getting familiar with the complex user interfaces of authoring tools. Natural Language Interface (NLI) presents promisi…
Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension
Ying Xu, Dakuo Wang, Mo Yu +15
Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity o…
Are Shortest Rationales the Best Explanations for Human Understanding?
Hua Shen, Tongshuang Wu, Wenbo Guo +1
Existing self-explaining models typically favor extracting the shortest possible rationales - snippets of an input text "responsible for" corresponding output - to explain the mode…
StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement
Zheng Zhang, Ying Xu, Yanhao Wang +6
Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with…
PromptChainer: Chaining Large Language Model Prompts through Visual Programming
Tongshuang Wu, Ellen Jiang, Aaron Donsbach +4
While LLMs can effectively help prototype single ML functionalities, many real-world applications involve complex tasks that cannot be easily handled via a single run of an LLM. Re…