CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding

21 December 2022

Papers citing "CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding"

6 / 6 papers shown

Title
Autoformalization with Large Language Models Yuhuai Wu Albert Q. Jiang Wenda Li M. Rabe Charles Staats M. Jamnik Christian Szegedy AI4CE 108 107 0 25 May 2022
Self-Consistency Improves Chain of Thought Reasoning in Language Models Xuezhi Wang Jason W. Wei Dale Schuurmans Quoc Le Ed H. Chi Sharan Narang Aakanksha Chowdhery Denny Zhou ReLM BDL LRM AI4CE 297 3,163 0 21 Mar 2022
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models Jason W. Wei Xuezhi Wang Dale Schuurmans Maarten Bosma Brian Ichter F. Xia Ed H. Chi Quoc Le Denny Zhou LM&Ro LRM AI4CE ReLM 315 8,261 0 28 Jan 2022
Deduplicating Training Data Makes Language Models Better Katherine Lee Daphne Ippolito A. Nystrom Chiyuan Zhang Douglas Eck Chris Callison-Burch Nicholas Carlini SyDa 234 447 0 14 Jul 2021
Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning Xiangyu Peng Siyan Li Sarah Wiegreffe Mark O. Riedl LRM 38 38 0 04 May 2021
Neurosymbolic AI: The 3rd Wave Artur Garcez Luís C. Lamb NAI 57 232 0 10 Dec 2020