The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models

Language models are few-shot learners

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901

Earlier work this paper cites.

Mixtures of probabilistic principal component analyzers

Michael E Tipping and Christopher M Bishop. 1999 · 1999

Earlier work this paper cites.

Age and gender differences in computer use and attitudes among secondary school students: what has changed?

Ann Colley and Chris Comber. 2003 · 2003

Earlier work this paper cites.

Factors predicting the use of technology: findings from the center for research and education on aging and technology enhancement (create)

Sara J Czaja, Neil Charness, Arthur D Fisk, Christopher Hertzog, Sankaran N Nair, Wendy A Rogers, and Joseph Sharit. 2006 · 2006

Earlier work this paper cites.

Technology to support aging in place: Older adults’ perspectives

Shengzhi Wang, Khalisa Bolling, Wenlin Mao, Jennifer Reichstadt, Dilip Jeste, Ho-Cheol Kim, and Camille Nebeker. 2019 · 2019

Earlier work this paper cites.

World values survey: Round seven – country-pooled datafile

C. Haerpfer, R. Inglehart, A. Moreno, C. Welzel, K. Kizilova, Diez-Medrano J., M. Lagos, P. Norris, E. Ponarin, and B. Puranen et al. 2020 · 2020

Earlier work this paper cites.

Artificial intelligence (ai): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Yogesh K Dwivedi, Laurie Hughes, Elvira Ismagilova, Gert Aarts, Crispin Coombs, Tom Crick, Yanqing Duan, Rohita Dwivedi, John Edwards, Aled Eirug, et al. 2021 · 2021

Earlier work this paper cites.

Scaling instruction-finetuned language models

Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022

Earlier work this paper cites.

Training language models to follow instructions with human feedback

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022

Earlier work this paper cites.

Which humans?

Mohammad Atari, Mona J Xue, Peter S Park, Damián E Blasi, and Joseph Henrich. 2023 · 2023

Earlier work this paper cites.

How (not) to use sociodemographic information for subjective nlp tasks

Original

Tilman Beck, Hendrik Schuff, Anne Lauscher, and Iryna Gurevych. 2023 · 2023

Cited alongside, same era.

Gender, age, and technology education influence the adoption and appropriation of llms

Original

Fiona Draxler, Daniel Buschek, Mikke Tavast, Perttu Hämäläinen, Albrecht Schmidt, Juhi Kulshrestha, and Robin Welsch. 2023 · 2023

Cited alongside, same era.

Opiniongpt: Modelling explicit biases in instruction-tuned llms

Original

Patrick Haller, Ansar Aynetdinov, and Alan Akbik. 2023 · 2023

Cited alongside, same era.

Why do older adults feel negatively about artificial intelligence products? an empirical study based on the perspectives of mismatches

Wenjia Hong, Changyong Liang, Yiming Ma, and Junhong Zhu. 2023 · 2023

Cited alongside, same era.

Mistral 7b

You don’t need a personality test to know these models are unreliable: Assessing the reliability of large language models on psychometric instruments

Original

Bangzhao Shu, Lechen Zhang, Minje Choi, Lavinia Dunagan, Dallas Card, and David Jurgens. 2023 · 2023

Later among the works it cites.

Aligning with whom? large language models have gender and racial biases in subjective nlp tasks

Original

Huaman Sun, Jiaxin Pei, Minje Choi, and David Jurgens. 2023 · 2023

Later among the works it cites.

A new open source flan 20b with ul2

Yi Tay. 2023 · 2023

Later among the works it cites.

"the large language model (llm) bias evaluation (age bias)" –dikwp research group international standard evaluation

Yucong Duan, Fuliang Tang, Kunguang Wu, Zhendong Guo, Shuaishuai Huang, Yingtian Mei, Yuxing Wang, Zeyu Yang, and Shiming Gong. 2024 · 2024

Closest in time.

Evaluating biases in context-dependent health questions

Original