ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2005.11739
  4. Cited By
Adversarial NLI for Factual Correctness in Text Summarisation Models

Adversarial NLI for Factual Correctness in Text Summarisation Models

24 May 2020
Mario Barrantes
Benedikt Herudek
Richard Wang
ArXiv (abs)PDFHTML

Papers citing "Adversarial NLI for Factual Correctness in Text Summarisation Models"

12 / 12 papers shown
Title
Using Similarity to Evaluate Factual Consistency in Summaries
Using Similarity to Evaluate Factual Consistency in Summaries
Yuxuan Ye
Edwin Simpson
Raul Santos Rodriguez
HILM
120
4
0
23 Sep 2024
Safeguarding Large Language Models: A Survey
Safeguarding Large Language Models: A Survey
Yi Dong
Ronghui Mu
Yanghao Zhang
Siqi Sun
Tianle Zhang
...
Yi Qi
Jinwei Hu
Jie Meng
Saddek Bensalem
Xiaowei Huang
OffRLKELMAILaw
174
52
0
03 Jun 2024
RAGTruth: A Hallucination Corpus for Developing Trustworthy
  Retrieval-Augmented Language Models
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL), 2023
Cheng Niu
Yuanhao Wu
Juno Zhu
Siliang Xu
Kashun Shum
Randy Zhong
Juntong Song
Tong Zhang
HILM
269
162
0
31 Dec 2023
Evaluating Large Language Models for Health-related Queries with
  Presuppositions
Evaluating Large Language Models for Health-related Queries with PresuppositionsAnnual Meeting of the Association for Computational Linguistics (ACL), 2023
Navreet Kaur
Monojit Choudhury
Danish Pruthi
HILMELM
178
11
0
14 Dec 2023
A Survey on Hallucination in Large Language Models: Principles,
  Taxonomy, Challenges, and Open Questions
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
Lei Huang
Weijiang Yu
Weitao Ma
Weihong Zhong
Zhangyin Feng
...
Qianglong Chen
Weihua Peng
Xiaocheng Feng
Bing Qin
Ting Liu
LRMHILM
262
1,625
0
09 Nov 2023
A Critical Evaluation of Evaluations for Long-form Question Answering
A Critical Evaluation of Evaluations for Long-form Question AnsweringAnnual Meeting of the Association for Computational Linguistics (ACL), 2023
Fangyuan Xu
Yixiao Song
Mohit Iyyer
Eunsol Choi
ELM
216
126
0
29 May 2023
SWING: Balancing Coverage and Faithfulness for Dialogue Summarization
SWING: Balancing Coverage and Faithfulness for Dialogue SummarizationFindings (Findings), 2023
Kung-Hsiang Huang
Siffi Singh
Xiaofei Ma
Wei Xiao
Wei Xiao
Nicholas Dingwall
William Yang Wang
Kathleen McKeown
HILM
142
14
0
25 Jan 2023
Just ClozE! A Novel Framework for Evaluating the Factual Consistency
  Faster in Abstractive Summarization
Just ClozE! A Novel Framework for Evaluating the Factual Consistency Faster in Abstractive Summarization
Yiyang Li
Lei Li
Marina Litvak
N. Vanetik
Dingxing Hu
Yuze Li
Yanquan Zhou
HILM
164
0
0
06 Oct 2022
Don't Say What You Don't Know: Improving the Consistency of Abstractive
  Summarization by Constraining Beam Search
Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam SearchIEEE Games Entertainment Media Conference (GEM), 2022
Daniel King
Zejiang Shen
Nishant Subramani
Daniel S. Weld
Iz Beltagy
Doug Downey
HILM
160
32
0
16 Mar 2022
Faithfulness in Natural Language Generation: A Systematic Survey of
  Analysis, Evaluation and Optimization Methods
Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods
Wei Li
Wenhao Wu
Moye Chen
Jiachen Liu
Xinyan Xiao
Hua Wu
HILM
231
36
0
10 Mar 2022
QAFactEval: Improved QA-Based Factual Consistency Evaluation for
  Summarization
QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization
Alexander R. Fabbri
Chien-Sheng Wu
Wenhao Liu
Caiming Xiong
HILM
178
246
0
16 Dec 2021
Investigating Crowdsourcing Protocols for Evaluating the Factual
  Consistency of Summaries
Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries
Xiangru Tang
Alexander R. Fabbri
Haoran Li
Ziming Mao
Griffin Adams
Borui Wang
Asli Celikyilmaz
Yashar Mehdad
Dragomir R. Radev
HILM
171
25
0
19 Sep 2021
1