15 citations · 24 across the 5 of their papers we have counts for
6 papers
RELIC: Evaluating Complex Reasoning via the Recognition of Languages In-Context
Jackson Petty, Michael Y. Hu, Wentao Wang +3
Large language models (LLMs) are increasingly used to solve complex tasks where they must retrieve and compose many pieces of in-context information in long reasoning chains. For m…
Rapid Word Learning Through Meta In-Context Learning
Wentao Wang, Guangyuan Jiang, Tal Linzen +1
Humans can quickly learn a new word from a few illustrative examples, and then systematically and flexibly use it in novel contexts. Yet the abilities of current language models fo…
A systematic investigation of learnability from single child linguistic input
Yulu Qin, Wentao Wang, Brenden M. Lake
Language models (LMs) have demonstrated remarkable proficiency in generating linguistically coherent text, sparking discussions about their relevance to understanding human languag…
Self-supervised learning of video representations from a child's perspective
A. Emin Orhan, Wentao Wang, Alex N. Wang +2
Children learn powerful internal models of the world around them from a few years of egocentric visual experience. Can such internal models be learned from a child's visual experie…
Data-to-Text Generation with Style Imitation
Shuai Lin, Wentao Wang, Zichao Yang +4
Recent neural approaches to data-to-text generation have mostly focused on improving content fidelity while lacking explicit control over writing styles (e.g., word choices, senten…
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
Zhiting Hu, Haoran Shi, Bowen Tan +12
We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, su…