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Social scientists quickly adopted large language models due to their ability to annotate documents without supervised training, an ability known as zero-shot learning.
A large-scale dataset for argument quality ranking: Construction and analysis
Gretz, S., R. Friedman, E. Cohen-Karlik, A. Toledo, D. Lahav, R. Aharonov, and N. Slonim (2019) · 1911
Earlier work this paper cites.
Policy agendas project: Supreme court cases
Bird, C., M. Whyman, B. D. Jones, F. R. Baumgartner, S. M. Theriault, D. A. Epp, C. Lee, and M. E. Sullivan (2009) · 2009
Earlier work this paper cites.
Kennedy, C. J., G. Bacon, A. Sahn, and C. von Vacano (2020) · 2009
Earlier work this paper cites.
Social conflict in africa: A new database
Salehyan, I., C. S. Hendrix, J. Hamner, C. Case, C. Linebarger, E. Stull, and J. Williams (2012) · 2012
Earlier work this paper cites.
Stance classification of context-dependent claims
Bar-Haim, R., I. Bhattacharya, F. Dinuzzo, A. Saha, and N. Slonim (2017, April) · 2017
Earlier work this paper cites.
A dataset for multi-target stance detection
Sobhani, P., D. Inkpen, and X. Zhu (2017) · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., M.-W. Chang, K. Lee, and K. Toutanova (2018) · 2018
Earlier work this paper cites.
Superglue: A stickier benchmark for general-purpose language understanding systems
Wang, A., Y. Pruksachatkun, N. Nangia, A. Singh, J. Michael, F. Hill, O. Levy, and S. Bowman (2019) · 2019
Earlier work this paper cites.
Experiment tracking with weights and biases
Biewald, L. (2020) · 2020
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The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation
Chicco, D. and G. Jurman (2020) · 2020
Earlier work this paper cites.
Detecting stance in media on global warming
Luo, Y., D. Card, and D. Jurafsky (2020, November) · 2020
Earlier work this paper cites.
Transformers: State-of-the-art natural language processing
Wolf, T., L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, et al. (2020) · 2020
Earlier work this paper cites.
Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing
He, P., J. Gao, and W. Chen (2021) · 2021
Earlier work this paper cites.
Datasets: A community library for natural language processing
Lhoest, Q., A. V. del Moral, P. von Platen, T. Wolf, M. Sasko, Y. Jernite, A. Thakur, L. Tunstall, S. Patil, M. Drame, et al. (2021) · 2021
Earlier work this paper cites.
Deep learning–based text classification: a comprehensive review
Minaee, S., N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu, and J. Gao (2021) · 2021
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Perceived risk, political polarization, and the willingness to follow covid-19 mitigation guidelines
Block Jr., R., M. Burnham, K. Kahn, R. Peng, J. Seeman, and C. Seto (2022) · 2022
Cited alongside, same era.
Polibertweet: A pre-trained language model for analyzing political content on twitter
Kawintiranon, K. and L. Singh (2022) · 2022
Cited alongside, same era.
Less annotating, more classifying – addressing the data scarcity issue of supervised machine learning with deep transfer learning and bert - nli
Laurer, M., W. v. Atteveldt, A. S. Casas, and K. Welbers (2022) · 2022
Cited alongside, same era.
Finetuned language models are zero-shot learners
Wei, J., M. Bosma, V. Y. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le (2022) · 2022
Supreme court database
Spaeth, H. K., L. Epstein, A. D. Martin, J. A. Segal, T. J. Ruger, and S. C. Benesh (2023) · 2023
Later among the works it cites.
Why open-source generative ai models are an ethical way forward for science
Spirling, A. (2023) · 2023
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Llama: Open and efficient foundation language models
Touvron, H., T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al. (2023) · 2023
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Llama 3 model card
AI@Meta (2024) · 2024
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Claude 3.5 sonnet
Anthropic (2024) · 2024
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Generational differences in the meaning of ‘democracy‘ and its conserquences for diffuse support in the united states
Berkman, M. B. and E. Plutzer (2024) · 2024
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Cited alongside, same era.
HEAT Map: Adl h.e.a.t. map
Anti-Defamation League (2023) · 2023
Cited alongside, same era.
Dynamics of polarizing rhetoric in congressional tweets
Ballard, A. O., R. DeTamble, S. Dorsey, M. Heseltine, and M. Johnson (2023) · 2023
Cited alongside, same era.
The matthews correlation coefficient (mcc) should replace the roc auc as the standard metric for assessing binary classification
Chicco, D. and G. Jurman (2023) · 2023
Cited alongside, same era.
Chatgpt outperforms crowd-workers for text-annotation tasks
Gilardi, F., M. Alizadeh, and M. Kubli (2023) · 2023
Cited alongside, same era.
Policy agendas project: State of the union speeches
Jones, B. D., F. R. Baumgartner, S. M. Theriault, D. A. Epp, R. Eissler, C. Lee, and M. E. Sullivan (2023) · 2023
Cited alongside, same era.
Building Efficient Universal Classifiers with Natural Language Inference
Laurer, M., W. van Atteveldt, A. Casas, and K. Welbers (2023, December) · 2023
Cited alongside, same era.
Gpt-4 technical report
OpenAI (2023) · 2023
Cited alongside, same era.
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Stance detection: A practical guide to classifying political beliefs in text
Burnham, M. (2024) · 2024
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A survey on evaluation of large language models
Chang, Y., X. Wang, J. Wang, Y. Wu, L. Yang, K. Zhu, H. Chen, X. Yi, C. Wang, Y. Wang, et al. (2024) · 2024
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Measuring the persuasiveness of language models
Durmus, E., L. Lovitt, A. Tamkin, S. Ritchie, J. Clark, and D. Ganguli (2024) · 2024
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Large language models: A survey
Minaee, S., T. Mikolov, N. Nikzad, M. Chenaghlu, R. Socher, X. Amatriain, and J. Gao (2024) · 2024
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Using proprietary language models in academic research requires explicit justification
Palmer, A., N. A. Smith, and A. Spirling (2024) · 2024
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Global terrorism database
START (2022) · 2024
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Syed, U., E. Light, X. Guo, L. Q. Huan Zhanga, Y. Ouyang, and B. Hu (2024) · 2024
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Can large language models transform computational social science?
Ziems, C., W. Held, O. Shaikh, J. Chen, Z. Zhang, and D. Yang (2024) · 2024
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