Project Contributors

Works Cited

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922

Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 5185–5198). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.463

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach (arXiv:1907.11692). arXiv. https://doi.org/10.48550/arXiv.1907.11692

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. Arxiv.

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 [Cs]. http://arxiv.org/abs/1810.04805

Rosen, Z. P., & Dale, R. (2023). BERTs of a feather: Studying inter- and intra-group communication via information theory and language models. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02267-2

Rosen, Z. P., & Dale, R. (2024). LLMs Don’t “Do Things with Words” but Their Lack of Illocution Can Inform the Study of Human Discourse. Proceedings of the Annual Meeting of the Cognitive Science Society, 46(0). https://escholarship.org/uc/item/25k7z0mz

Titus, L. M. (2024). Does ChatGPT have semantic understanding? A problem with the statistics-of-occurrence strategy. Cognitive Systems Research, 83, 101174. https://doi.org/10.1016/j.cogsys.2023.101174

Utsumi, A. (2020). Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis. Cognitive Science, 44(6), e12844. https://doi.org/10.1111/cogs.12844

Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90–112. https://doi.org/10.1111/bjet.13370