Fetching the paper…
Reading the bibliography…
Generative AI (GenAI) is increasingly used in survey contexts to simulate human preferences.
Die deutsche Wahlforschung und die German Longitudinal Election Study (GLES) , pages 141–172
Rüdiger Schmitt-Beck, Hans Rattinger, Sigrid Roßteutscher, and Bernhard Weßels · 2010
Earlier work this paper cites.
A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, et al · 2021
Earlier work this paper cites.
Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space
Mor Geva, Avi Caciularu, Kevin Ro Wang, and Yoav Goldberg · 2022
Earlier work this paper cites.
Out of one, many: Using language models to simulate human samples
Lisa P Argyle, Ethan C Busby, Nancy Fulda, Joshua R Gubler, Christopher Rytting, and David Wingate · 2023
Earlier work this paper cites.
AI-augmented surveys: Leveraging large language models and surveys for opinion prediction
Junsol Kim and Byungkyu Lee · 2023
Earlier work this paper cites.
Synthetic replacements for human survey data? The perils of large language models
James Bisbee, Joshua D Clinton, Cassy Dorff, Brenton Kenkel, and Jennifer M Larson · 2023
Earlier work this paper cites.
Using GPT for market research
James Brand, Ayelet Israeli, and Donald Ngwe · 2023
Earlier work this paper cites.
Petter Törnberg · 2023
Earlier work this paper cites.
Using large language models to simulate multiple humans and replicate human subject studies
Gati V Aher, Rosa I Arriaga, and Adam Tauman Kalai · 2023
Earlier work this paper cites.
Evaluating large language models in generating synthetic HCI research data: A case study
Perttu Hämäläinen, Mikke Tavast, and Anton Kunnari · 2023
Earlier work this paper cites.
Whose opinions do language models reflect?
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto · 2023
Earlier work this paper cites.
The language of prompting: What linguistic properties make a prompt successful?
Alina Leidinger, Robert Van Rooij, and Ekaterina Shutova · 2023
Earlier work this paper cites.
State of what art? A call for multi-prompt llm evaluation
Moran Mizrahi, Guy Kaplan, Dan Malkin, Rotem Dror, Dafna Shahaf, and Gabriel Stanovsky · 2023
Earlier work this paper cites.
Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr · 2023
Cited alongside, same era.
Promptrobust: Towards evaluating the robustness of large language models on adversarial prompts
Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Yue Zhang, Neil Gong, et al · 2023
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Cited alongside, same era.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2023
Cited alongside, same era.
Can large language models transform computational social science?
Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang · 2024
Later among the works it cites.
Can large language model agents simulate human trust behaviors?
Chengxing Xie, Canyu Chen, Feiran Jia, Ziyu Ye, Kai Shu, Adel Bibi, Ziniu Hu, Philip Torr, Bernard Ghanem, and Guohao Li · 2024
Later among the works it cites.
Posix: A prompt sensitivity index for large language models
Anwoy Chatterjee, HSVNS Kowndinya Renduchintala, Sumit Bhatia, and Tanmoy Chakraborty · 2024
Later among the works it cites.
Mind your format: Towards consistent evaluation of in-context learning improvements
Anton Voronov, Lena Wolf, and Max Ryabinin · 2024
Later among the works it cites.
ProSA: Assessing and understanding the prompt sensitivity of LLMs
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Attention is all you need, 2023
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2023
Cited alongside, same era.
Steering Llama 2 via contrastive activation addition
Nina Panickssery, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Matt Turner · 2023
Cited alongside, same era.
Leah von der Heyde, Anna-Carolina Haensch, and Alexander Wenz · 2024
Cited alongside, same era.
A large-scale empirical study on large language models for election prediction
Chenxiao Yu, Zhaotian Weng, Yuangang Li, Zheng Li, Xiyang Hu, and Yue Zhao · 2024
Cited alongside, same era.
Generative agent simulations of 1,000 people
Joon Sung Park, Carolyn Q Zou, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Ringel Morris, Robb Willer, Percy Liang, and Michael S Bernstein · 2024
Cited alongside, same era.
Performance and biases of large language models in public opinion simulation
Yao Qu and Jue Wang · 2024
Cited alongside, same era.
Leah von der Heyde, Anna-Carolina Haensch, and Alexander Wenz · 2024
Cited alongside, same era.
Large language models cannot replace human participants because they cannot portray identity groups
Angelina Wang, Jamie Morgenstern, and John P Dickerson · 2024
Cited alongside, same era.
Jingming Zhuo, Songyang Zhang, Xinyu Fang, Haodong Duan, Dahua Lin, and Kai Chen · 2024
Later among the works it cites.
Llama 3.2: Revolutionizing edge AI and vision with open, customizable models, 2024a
MetaAI · 2024
Later among the works it cites.
Gemma: Introducing new state-of-the-art open models, 2024
Google · 2024
Later among the works it cites.
German Longitudinal Election Study (GLES), 2024
GESIS – Leibniz Institute for the Social Sciences · 2024
Later among the works it cites.
Wahl-O-Mat, 2025
Bundeszentrale für politische Bildung · 2024
Later among the works it cites.
A mechanistic understanding of alignment algorithms: A case study on dpo and toxicity
Andrew Lee, Xiaoyan Bai, Itamar Pres, Martin Wattenberg, Jonathan K Kummerfeld, and Rada Mihalcea · 2024
Later among the works it cites.
Questioning the survey responses of large language models
Ricardo Dominguez-Olmedo, Moritz Hardt, and Celestine Mendler-Dünner · 2025
Closest in time.
Qwen2.5 technical report, 2025
An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tianyi Tang, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu · 2025
Closest in time.