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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

9 November 2023
Lei Huang
Weijiang Yu
Weitao Ma
Weihong Zhong
Zhangyin Feng
Haotian Wang
Qianglong Chen
Weihua Peng
Xiaocheng Feng
Bing Qin
Ting Liu
    LRM
    HILM
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Papers citing "A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions"

15 / 165 papers shown
Title
ReAct: Synergizing Reasoning and Acting in Language Models
ReAct: Synergizing Reasoning and Acting in Language Models
Shunyu Yao
Jeffrey Zhao
Dian Yu
Nan Du
Izhak Shafran
Karthik R. Narasimhan
Yuan Cao
LLMAG
ReLM
LRM
273
2,510
0
06 Oct 2022
Generate rather than Retrieve: Large Language Models are Strong Context
  Generators
Generate rather than Retrieve: Large Language Models are Strong Context Generators
Wenhao Yu
Dan Iter
Shuohang Wang
Yichong Xu
Mingxuan Ju
Soumya Sanyal
Chenguang Zhu
Michael Zeng
Meng Jiang
RALM
AIMat
237
323
0
21 Sep 2022
Large Language Models are Zero-Shot Reasoners
Large Language Models are Zero-Shot Reasoners
Takeshi Kojima
S. Gu
Machel Reid
Yutaka Matsuo
Yusuke Iwasawa
ReLM
LRM
328
4,139
0
24 May 2022
Training language models to follow instructions with human feedback
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
12,003
0
04 Mar 2022
Deduplicating Training Data Makes Language Models Better
Deduplicating Training Data Makes Language Models Better
Katherine Lee
Daphne Ippolito
A. Nystrom
Chiyuan Zhang
Douglas Eck
Chris Callison-Burch
Nicholas Carlini
SyDa
242
595
0
14 Jul 2021
The Factual Inconsistency Problem in Abstractive Text Summarization: A
  Survey
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
Yi-Chong Huang
Xiachong Feng
Xiaocheng Feng
Bing Qin
HILM
136
105
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
231
306
0
27 Apr 2021
Entity-level Factual Consistency of Abstractive Text Summarization
Entity-level Factual Consistency of Abstractive Text Summarization
Feng Nan
Ramesh Nallapati
Zhiguo Wang
Cicero Nogueira dos Santos
Henghui Zhu
Dejiao Zhang
Kathleen McKeown
Bing Xiang
HILM
144
158
0
18 Feb 2021
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Leo Gao
Stella Biderman
Sid Black
Laurence Golding
Travis Hoppe
...
Horace He
Anish Thite
Noa Nabeshima
Shawn Presser
Connor Leahy
AIMat
282
2,000
0
31 Dec 2020
Extracting Training Data from Large Language Models
Extracting Training Data from Large Language Models
Nicholas Carlini
Florian Tramèr
Eric Wallace
Matthew Jagielski
Ariel Herbert-Voss
...
Tom B. Brown
D. Song
Ulfar Erlingsson
Alina Oprea
Colin Raffel
MLAU
SILM
290
1,824
0
14 Dec 2020
Towards Faithful Neural Table-to-Text Generation with Content-Matching
  Constraints
Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints
Zhenyi Wang
Xiaoyang Wang
Bang An
Dong Yu
Changyou Chen
LMTD
168
84
0
03 May 2020
Scaling Laws for Neural Language Models
Scaling Laws for Neural Language Models
Jared Kaplan
Sam McCandlish
T. Henighan
Tom B. Brown
B. Chess
R. Child
Scott Gray
Alec Radford
Jeff Wu
Dario Amodei
264
4,505
0
23 Jan 2020
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
449
2,589
0
03 Sep 2019
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,675
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,145
0
06 Jun 2015
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