Improving Factuality of Abstractive Summarization via Contrastive Reward
Learning

Improving Factuality of Abstractive Summarization via Contrastive Reward Learning

10 July 2023

Graham Neubig

Papers citing "Improving Factuality of Abstractive Summarization via Contrastive Reward Learning"

9 / 9 papers shown

Title
MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration David Wan Justin Chih-Yao Chen Elias Stengel-Eskin Joey Tianyi Zhou LLMAG LRM 65 1 0 19 Mar 2025
Learning to Substitute Words with Model-based Score Ranking Hongye Liu Ricardo Henao 43 0 0 09 Feb 2025
Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models Qitan Lv Jie Wang Hanzhu Chen Bin Li Yongdong Zhang Feng Wu HILM 31 3 0 19 Oct 2024
Multi-Dimensional Optimization for Text Summarization via Reinforcement Learning Sangwon Ryu Heejin Do Yunsu Kim Gary Geunbae Lee Jungseul Ok 31 3 0 01 Jun 2024
A Survey of Large Language Models in Medicine: Progress, Application, and Challenge Hongjian Zhou Fenglin Liu Boyang Gu Xinyu Zou Jinfa Huang ... Yefeng Zheng Lei A. Clifton Zheng Li Fenglin Liu David A. Clifton LM&MA 41 108 0 09 Nov 2023
Chain-of-Verification Reduces Hallucination in Large Language Models S. Dhuliawala M. Komeili Jing Xu Roberta Raileanu Xian Li Asli Celikyilmaz Jason Weston LRM HILM 22 177 0 20 Sep 2023
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 369 12,081 0 04 Mar 2022
Factual Error Correction for Abstractive Summarization Models Mengyao Cao Yue Dong Jiapeng Wu Jackie C.K. Cheung HILM KELM 169 159 0 17 Oct 2020
Teaching Machines to Read and Comprehend Karl Moritz Hermann Tomás Kociský Edward Grefenstette L. Espeholt W. Kay Mustafa Suleyman Phil Blunsom 211 3,515 0 10 Jun 2015