Britain's Mood, Entailed Weekly: In Silico Longitudinal Surveys with Fine-Tuned Large Language Models-Reference-Cited by-同舟云学术

Britain's Mood, Entailed Weekly: In Silico Longitudinal Surveys with Fine-Tuned Large Language Models

Published:2024-05-21 Issue: Volume: Page:47-50
ISSN:
Container-title:Companion Proceedings of the 16th ACM Web Science Conference
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Author:

Ahnert Georg¹^ORCID,Pellert Max²^ORCID,Garcia David³^ORCID,Strohmaier Markus⁴^ORCID

Affiliation:

1. University of Mannheim, Germany

2. University of Konstanz, Germany

3. University of Konstanz, Germany and Complexity Science Hub, Austria

4. University of Mannheim, Germany and GESIS - Leibniz Institute for the Social Sciences, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3630744.3659829

Reference18 articles.

1. Out of One, Many: Using Language Models to Simulate Human Samples

2. Mohammad Atari Mona J. Xue Peter S. Park Damián Ezequiel Blasi and Joseph Henrich. 2023. Which Humans?preprint. PsyArXiv. https://doi.org/10.31234/osf.io/5b26t

3. Tilman Beck, Hendrik Schuff, Anne Lauscher, and Iryna Gurevych. 2024. Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics, Vol. 1: Long Papers. Association for Computational Linguistics, St. Julian’s, Malta, 2589–2615. https://aclanthology.org/2024.eacl-long.159

4. James Bisbee Joshua Clinton Cassy Dorff Brenton Kenkel and Jennifer Larson. 2023. Synthetic Replacements for Human Survey Data? The Perils of Large Language Models. preprint. SocArXiv. https://doi.org/10.31235/osf.io/5ecfa

5. Jose Camacho-Collados Kiamehr Rezaee Talayeh Riahi Asahi Ushio Daniel Loureiro Dimosthenis Antypas Joanne Boisson Luis Espinosa-Anke Fangyu Liu Eugenio Martínez-Cámara Gonzalo Medina Thomas Buhrmann Leonardo Neves and Francesco Barbieri. 2022. TweetNLP: Cutting-Edge Natural Language Processing for Social Media. http://arxiv.org/abs/2206.14774 arXiv:2206.14774 [cs].