ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2304.03738
  4. Cited By
Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language
  Models

Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models

7 April 2023
Emilio Ferrara
    SILM
ArXivPDFHTML

Papers citing "Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models"

16 / 116 papers shown
Title
Learning Linear Causal Representations from Interventions under General
  Nonlinear Mixing
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
Simon Buchholz
Goutham Rajendran
Elan Rosenfeld
Bryon Aragam
Bernhard Schölkopf
Pradeep Ravikumar
CML
34
58
0
04 Jun 2023
ChatGPT is a Remarkable Tool -- For Experts
ChatGPT is a Remarkable Tool -- For Experts
A. Azaria
Rina Azoulay-Schwartz
S. Reches
24
58
0
02 Jun 2023
A Survey on Large Language Models for Recommendation
A Survey on Large Language Models for Recommendation
Likang Wu
Zhilan Zheng
Zhaopeng Qiu
Hao Wang
Hongchao Gu
...
Chen Zhu
Hengshu Zhu
Qi Liu
Hui Xiong
Enhong Chen
41
358
0
31 May 2023
Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4
  mirroring math anxiety in high-school students
Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school students
Katherine Abramski
Salvatore Citraro
Luigi Lombardi
Giulio Rossetti
Massimo Stella
23
5
0
22 May 2023
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Sébastien Bubeck
Varun Chandrasekaran
Ronen Eldan
J. Gehrke
Eric Horvitz
...
Scott M. Lundberg
Harsha Nori
Hamid Palangi
Marco Tulio Ribeiro
Yi Zhang
ELM
AI4MH
AI4CE
ALM
298
2,232
0
22 Mar 2023
ChatGPT: Jack of all trades, master of none
ChatGPT: Jack of all trades, master of none
Jan Kocoñ
Igor Cichecki
Oliwier Kaszyca
Mateusz Kochanek
Dominika Szydło
...
Maciej Piasecki
Lukasz Radliñski
Konrad Wojtasik
Stanislaw Wo'zniak
Przemyslaw Kazienko
AI4MH
37
527
0
21 Feb 2023
Evolutionary Generalized Zero-Shot Learning
Evolutionary Generalized Zero-Shot Learning
Dubing Chen
Haofeng Zhang
Yang Long
VLM
34
1
0
23 Nov 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
328
11,953
0
04 Mar 2022
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Jason W. Wei
Xuezhi Wang
Dale Schuurmans
Maarten Bosma
Brian Ichter
F. Xia
Ed H. Chi
Quoc Le
Denny Zhou
LM&Ro
LRM
AI4CE
ReLM
389
8,495
0
28 Jan 2022
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
279
1,996
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
1,815
0
14 Dec 2020
Fine-Tuning Language Models from Human Preferences
Fine-Tuning Language Models from Human Preferences
Daniel M. Ziegler
Nisan Stiennon
Jeff Wu
Tom B. Brown
Alec Radford
Dario Amodei
Paul Christiano
G. Irving
ALM
286
1,595
0
18 Sep 2019
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
323
4,212
0
23 Aug 2019
Improving fairness in machine learning systems: What do industry
  practitioners need?
Improving fairness in machine learning systems: What do industry practitioners need?
Kenneth Holstein
Jennifer Wortman Vaughan
Hal Daumé
Miroslav Dudík
Hanna M. Wallach
FaML
HAI
192
742
0
13 Dec 2018
Word Translation Without Parallel Data
Word Translation Without Parallel Data
Alexis Conneau
Guillaume Lample
MarcÁurelio Ranzato
Ludovic Denoyer
Hervé Jégou
189
1,635
0
11 Oct 2017
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
257
3,684
0
28 Feb 2017
Previous
123