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With a Little Push, NLI Models can Robustly and Efficiently Predict
  Faithfulness

With a Little Push, NLI Models can Robustly and Efficiently Predict Faithfulness

26 May 2023
Julius Steen
Juri Opitz
Anette Frank
K. Markert
    HILM
ArXivPDFHTML

Papers citing "With a Little Push, NLI Models can Robustly and Efficiently Predict Faithfulness"

6 / 6 papers shown
Title
Natural Language Processing RELIES on Linguistics
Natural Language Processing RELIES on Linguistics
Juri Opitz
Shira Wein
Nathan Schneider
AI4CE
44
7
0
09 May 2024
Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable
  Features
Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features
Hannah Rashkin
David Reitter
Gaurav Singh Tomar
Dipanjan Das
149
101
0
14 Jul 2021
Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark
Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark
Nouha Dziri
Hannah Rashkin
Tal Linzen
David Reitter
ALM
185
79
0
30 Apr 2021
Understanding Factuality in Abstractive Summarization with FRANK: A
  Benchmark for Factuality Metrics
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics
Artidoro Pagnoni
Vidhisha Balachandran
Yulia Tsvetkov
HILM
215
305
0
27 Apr 2021
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language
  Understanding
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Alex Jinpeng Wang
Amanpreet Singh
Julian Michael
Felix Hill
Omer Levy
Samuel R. Bowman
ELM
294
6,943
0
20 Apr 2018
Teaching Machines to Read and Comprehend
Teaching Machines to Read and Comprehend
Karl Moritz Hermann
Tomás Kociský
Edward Grefenstette
L. Espeholt
W. Kay
Mustafa Suleyman
Phil Blunsom
170
3,508
0
10 Jun 2015
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