385 citations · 720 across the 6 of their papers we have counts for
15 papers
GenNI: Human-AI Collaboration for Data-Backed Text Generation
Hendrik Strobelt, Jambay Kinley, Robert Krueger +3
Table2Text systems generate textual output based on structured data utilizing machine learning. These systems are essential for fluent natural language interfaces in tools such as…
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…
Understanding the Role of Individual Units in a Deep Neural Network
David Bau, Jun-Yan Zhu, Hendrik Strobelt +3
Deep neural networks excel at finding hierarchical representations that solve complex tasks over large data sets. How can we humans understand these learned representations? In thi…
Semantic Photo Manipulation with a Generative Image Prior
David Bau, Hendrik Strobelt, William Peebles +4
Despite the recent success of GANs in synthesizing images conditioned on inputs such as a user sketch, text, or semantic labels, manipulating the high-level attributes of an existi…
Seeing What a GAN Cannot Generate
David Bau, Jun-Yan Zhu, Jonas Wulff +4
Despite the success of Generative Adversarial Networks (GANs), mode collapse remains a serious issue during GAN training. To date, little work has focused on understanding and quan…
exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models
Benjamin Hoover, Hendrik Strobelt, Sebastian Gehrmann
Large language models can produce powerful contextual representations that lead to improvements across many NLP tasks. Since these models are typically guided by a sequence of lear…