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Learning to Deceive Knowledge Graph Augmented Models via Targeted
  Perturbation

Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation

24 October 2020
Mrigank Raman
Aaron Chan
Siddhant Agarwal
Peifeng Wang
Hansen Wang
Sungchul Kim
Ryan Rossi
Handong Zhao
Nedim Lipka
Xiang Ren
ArXivPDFHTML

Papers citing "Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation"

4 / 4 papers shown
Title
Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answering
Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answering
Yuan Sui
Yufei He
Zifeng Ding
Bryan Hooi
HILM
ELM
RALM
68
7
0
20 Feb 2025
An Empirical Revisiting of Linguistic Knowledge Fusion in Language
  Understanding Tasks
An Empirical Revisiting of Linguistic Knowledge Fusion in Language Understanding Tasks
Changlong Yu
Tianyi Xiao
Lingpeng Kong
Yangqiu Song
Wilfred Ng
30
3
0
24 Oct 2022
ComFact: A Benchmark for Linking Contextual Commonsense Knowledge
ComFact: A Benchmark for Linking Contextual Commonsense Knowledge
Silin Gao
Jena D. Hwang
Saya Kanno
Hiromi Wakaki
Yuki Mitsufuji
Antoine Bosselut
HILM
30
16
0
23 Oct 2022
Language Models as Knowledge Bases?
Language Models as Knowledge Bases?
Fabio Petroni
Tim Rocktaschel
Patrick Lewis
A. Bakhtin
Yuxiang Wu
Alexander H. Miller
Sebastian Riedel
KELM
AI4MH
413
2,584
0
03 Sep 2019
1