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. 2202.12299
  4. Cited By
Capturing Failures of Large Language Models via Human Cognitive Biases

Capturing Failures of Large Language Models via Human Cognitive Biases

24 February 2022
Erik Jones
Jacob Steinhardt
ArXivPDFHTML

Papers citing "Capturing Failures of Large Language Models via Human Cognitive Biases"

12 / 12 papers shown
Title
Cognitive Debiasing Large Language Models for Decision-Making
Cognitive Debiasing Large Language Models for Decision-Making
Yougang Lyu
Shijie Ren
Yue Feng
Zihan Wang
Z. Chen
Z. Z. Ren
Maarten de Rijke
36
0
0
05 Apr 2025
Large Language Models and Cognitive Science: A Comprehensive Review of
  Similarities, Differences, and Challenges
Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges
Qian Niu
Junyu Liu
Ziqian Bi
Pohsun Feng
Benji Peng
...
Ming Li
Lawrence KQ Yan
Yichao Zhang
Caitlyn Heqi Yin
Cheng Fei
38
13
0
04 Sep 2024
LLM-based NLG Evaluation: Current Status and Challenges
LLM-based NLG Evaluation: Current Status and Challenges
Mingqi Gao
Xinyu Hu
Jie Ruan
Xiao Pu
Xiaojun Wan
ELM
LM&MA
53
29
0
02 Feb 2024
Concise and Organized Perception Facilitates Reasoning in Large Language Models
Concise and Organized Perception Facilitates Reasoning in Large Language Models
Junjie Liu
Shaotian Yan
Chen Shen
Zhengdong Xiao
Wenxiao Wang
Jieping Ye
Jieping Ye
LRM
8
1
0
05 Oct 2023
Using Large Language Models to Simulate Multiple Humans and Replicate
  Human Subject Studies
Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies
Gati Aher
RosaI. Arriaga
Adam Tauman Kalai
35
343
0
18 Aug 2022
Measuring Coding Challenge Competence With APPS
Measuring Coding Challenge Competence With APPS
Dan Hendrycks
Steven Basart
Saurav Kadavath
Mantas Mazeika
Akul Arora
...
Collin Burns
Samir Puranik
Horace He
D. Song
Jacob Steinhardt
ELM
AIMat
ALM
194
623
0
20 May 2021
What Makes Good In-Context Examples for GPT-$3$?
What Makes Good In-Context Examples for GPT-333?
Jiachang Liu
Dinghan Shen
Yizhe Zhang
Bill Dolan
Lawrence Carin
Weizhu Chen
AAML
RALM
275
1,311
0
17 Jan 2021
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
267
1,808
0
14 Dec 2020
GO FIGURE: A Meta Evaluation of Factuality in Summarization
GO FIGURE: A Meta Evaluation of Factuality in Summarization
Saadia Gabriel
Asli Celikyilmaz
Rahul Jha
Yejin Choi
Jianfeng Gao
HILM
233
96
0
24 Oct 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
275
1,583
0
18 Sep 2019
The Woman Worked as a Babysitter: On Biases in Language Generation
The Woman Worked as a Babysitter: On Biases in Language Generation
Emily Sheng
Kai-Wei Chang
Premkumar Natarajan
Nanyun Peng
206
615
0
03 Sep 2019
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
1