3 citations · 5 across the 6 of their papers we have counts for
6 papers
Scenarios and Approaches for Situated Natural Language Explanations
Pengshuo Qiu, Frank Rudzicz, Zining Zhu
Large language models (LLMs) can be used to generate natural language explanations (NLE) that are adapted to different users' situations. However, there is yet to be a quantitative…
Plug and Play with Prompts: A Prompt Tuning Approach for Controlling Text Generation
Rohan Deepak Ajwani, Zining Zhu, Jonathan Rose +1
Transformer-based Large Language Models (LLMs) have shown exceptional language generation capabilities in response to text-based prompts. However, controlling the direction of gene…
A State-Vector Framework for Dataset Effects
Esmat Sahak, Zining Zhu, Frank Rudzicz
The impressive success of recent deep neural network (DNN)-based systems is significantly influenced by the high-quality datasets used in training. However, the effects of the data…
Measuring Information in Text Explanations
Zining Zhu, Frank Rudzicz
Text-based explanation is a particularly promising approach in explainable AI, but the evaluation of text explanations is method-dependent. We argue that placing the explanations o…
CCGen: Explainable Complementary Concept Generation in E-Commerce
Jie Huang, Yifan Gao, Zheng Li +7
We propose and study Complementary Concept Generation (CCGen): given a concept of interest, e.g., "Digital Cameras", generating a list of complementary concepts, e.g., 1) Camera Le…
OOD-Probe: A Neural Interpretation of Out-of-Domain Generalization
Zining Zhu, Soroosh Shahtalebi, Frank Rudzicz
The ability to generalize out-of-domain (OOD) is an important goal for deep neural network development, and researchers have proposed many high-performing OOD generalization method…