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Sources

Every factual claim in LLMs in the Wild traces back to one of these 20 sources.

  1. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). arXiv:1810.04805. — pt. 1
  2. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research, 21(140), 1–67. arXiv:1910.10683. — pt. 1, 3
  3. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems 30 (NeurIPS 2017). arXiv:1706.03762. — pt. 1
  4. Jurafsky, D., & Martin, J. H. (2026). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models (3rd ed., draft of January 6, 2026). Stanford University. Ch. 7 (Large Language Models); Ch. 10 (Masked Language Models). — pt. 1, 3, 4, 5
  5. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems 33 (NeurIPS 2020). arXiv:2005.14165. — pt. 2
  6. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., et al. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35 (NeurIPS 2022). arXiv:2203.02155. — pt. 2
  7. Anthropic. System Prompts. Claude Platform Documentation (Release Notes). Accessed 2026-06-25. — pt. 2
  8. Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv:2106.09685. — pt. 2
  9. Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022). Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? In Proceedings of EMNLP 2022 (pp. 11048–11064). arXiv:2202.12837. — pt. 2
  10. Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., Presser, S., & Leahy, C. (2020). The Pile: An 800GB Dataset of Diverse Text for Language Modeling. arXiv:2101.00027. — pt. 3
  11. Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., et al. (2024). Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research. In Proceedings of ACL 2024 (Long Papers). arXiv:2402.00159. — pt. 3
  12. Longpre, S., Mahari, R., Lee, A., Lund, C., Oderinwale, H., Brannon, W., Saxena, N., et al. (2024). Consent in Crisis: The Rapid Decline of the AI Data Commons. Advances in Neural Information Processing Systems 37 (NeurIPS 2024). arXiv:2407.14933. — pt. 3
  13. Carlini, N., Tramèr, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, Ú., Oprea, A., & Raffel, C. (2021). Extracting Training Data from Large Language Models. In 30th USENIX Security Symposium (USENIX Security 21), pp. 2633–2650. arXiv:2012.07805. — pt. 3, 5
  14. Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., & Steinhardt, J. (2021). Measuring Massive Multitask Language Understanding. International Conference on Learning Representations (ICLR 2021). arXiv:2009.03300. — pt. 4
  15. Zhang, A. K., Klyman, K., Mai, Y., Levine, Y., Zhang, Y., Bommasani, R., & Liang, P. (2024). Language model developers should report train-test overlap. arXiv:2410.08385. (Position paper, ICML 2025.) — pt. 4
  16. Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., et al. (2023). Holistic Evaluation of Language Models (HELM). Transactions on Machine Learning Research (TMLR). arXiv:2211.09110. — pt. 4
  17. Cheng, M., Yu, S., Lee, C., Khadpe, P., Ibrahim, L., & Jurafsky, D. (2025). ELEPHANT: Measuring and Understanding Social Sycophancy in LLMs. arXiv:2505.13995. — pt. 5
  18. Nadeem, M., Bethke, A., & Reddy, S. (2021). StereoSet: Measuring Stereotypical Bias in Pretrained Language Models. In Proceedings of ACL-IJCNLP 2021 (Vol. 1: Long Papers, pp. 5356–5371). arXiv:2004.09456. — pt. 5
  19. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems 33 (NeurIPS 2020). arXiv:2005.11401. — pt. 5
  20. Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., et al. (2021). Ethical and social risks of harm from Language Models. DeepMind. arXiv:2112.04359. — pt. 5

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