[144] Salon. A racist stereotype is shattered: Study finds white youth are more likely to abuse hard drugs than black youth. https://www.salon.com/2016/04/06,this_racist_stereotype_is_shattered_study Finds_white_youth.are-more Likely_to_abuse-hard_drugs Than/black_youthpartner/, 2016.
[145] S. Santurkar, D. Tsipras, and A. Madry. Breeds: Benchmarks for subpopulation shift. International Conference On Learning Representations, 2020.
[146] R. Schaeffer, B. Miranda, and S. Koyejo. Are emergent abilities of large language models a mirage? arXiv preprint arXiv:2304.15004, 2023.
[147] H. Shao, J. Huang, S. Zheng, and K. C.-C. Chang. Quantifying association capabilities of large language models and its implications on privacy leakage. arXiv preprint arXiv:2305.12707, 2023.
[148] F. Shi, M. Suzgun, M. Freitag, X. Wang, S. Srivats, S. Vosoughi, H. W. Chung, Y. Tay, S. Ruder, D. Zhou, et al. Language models are multilingual chain-of-thought reasoners. arXiv preprint arXiv:2210.03057, 2022.
[149] W. Shi, R. Shea, S. Chen, C. Zhang, R. Jia, and Z. Yu. Just fine-tune twice: Selective differential privacy for large language models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6327-6340, Abu Dhabi, United Arab Emirates, Dec. 2022. Association for Computational Linguistics. URL https://aclanthology.org/2022.emnlp-main.425.
[150] T. Shin, Y. Razeghi, R. L. Logan IV, E. Wallace, and S. Singh. Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv, 2020.
[151] N. Shinn, B. Labash, and A. Gopinath. Reflection: an autonomous agent with dynamic memory and self-reflection. arXiv preprint arXiv: Arxiv-2303.11366, 2023.
[152] M. Shridhar, X. Yuan, M. Côté, Y. Bisk, A. Trischler, and M. J. Hausknecht. Alfworld: Aligning text and embodied environments for interactive learning. In 9th International Conference on Learning Representations, ICLR, 2021.
[153] C. Si, Z. Gan, Z. Yang, S. Wang, J. Wang, J. L. Boyd-Graber, and L. Wang. Prompting GPT-3 to be reliable. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=98p5x51L5af.
[154] R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1631-1642, Seattle, Washington, USA, Oct. 2013. Association for Computational Linguistics. URL https://aclanthology.org/D13-1170.
[155] I. Solaiman and C. Dennison. Process for adapting language models to society (palms) with values-targeted datasets. Advances in Neural Information Processing Systems, 34:5861-5873, 2021.
[156] A. Srivastava, A. Rastogi, A. Rao, A. A. M. Shoeb, A. Abid, A. Fisch, A. R. Brown, A. Santoro, A. Gupta, A. Garriga-Alonso, et al. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615, 2022.
[157] StabilityAI. StableVicuna: An RLHF Fine-Tune of Vicuna-13B v0. Available at https://github.com/StabilityAI/StableVicuna, 4 2023. URL https://stability.ai/blog/stablevicuna-open-source-rlhf-chatbot. DOI:10.57967/hf/0588.
[158] M. Suzgun, N. Scales, N. Scharli, S. Gehrmann, Y. Tay, H. W. Chung, A. Chowdhery, Q. V. Le, E. H. Chi, D. Zhou, et al. Challenging big-bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261, 2022.
[159] R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto. Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/stanford_alpaca, 2023.
[160] M. N. Team. Introducing mpt-7b: A new standard for open-source, by usable llms, 2023. URL www.mosaicml.com/blog/mpt-7b. Accessed: 2023-08-19.
[161] Teen Vogue. The fox-eye trend isn't cute—it's racist. https://www.teenvogue.com/story/fox-eye-trend-cultural-appropriation-asian-features, 2020.