📚 Бібліографічний список

Footnotes

1. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain of Thought Prompting Elicits Reasoning in Large Language Models. ↩

2. Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large Language Models are Zero-Shot Reasoners. ↩

3. Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2022). Self-Consistency Improves Chain of Thought Reasoning in Language Models. ↩

4. Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., & Chen, W. (2022). What Makes Good In-Context Examples for GPT-3? Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures. https://doi.org/10.18653/v1/2022.deelio-1.10 ↩ ↩²

5. Arora, S., Narayan, A., Chen, M. F., Orr, L., Guha, N., Bhatia, K., Chami, I., Sala, F., & Ré, C. (2022). Ask Me Anything: A simple strategy for prompting language models. ↩ ↩²

6. Liu, J., Liu, A., Lu, X., Welleck, S., West, P., Bras, R. L., Choi, Y., & Hajishirzi, H. (2021). Generated Knowledge Prompting for Commonsense Reasoning. ↩

7. Sun, Z., Wang, X., Tay, Y., Yang, Y., & Zhou, D. (2022). Recitation-Augmented Language Models. ↩

8. 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? ↩

9. Nye, M., Andreassen, A. J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., & Odena, A. (2021). Show Your Work: Scratchpads for Intermediate Computation with Language Models. ↩

10. Jung, J., Qin, L., Welleck, S., Brahman, F., Bhagavatula, C., Bras, R. L., & Choi, Y. (2022). Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations. ↩

11. Zelikman, E., Wu, Y., Mu, J., & Goodman, N. D. (2022). STaR: Bootstrapping Reasoning With Reasoning. ↩

12. Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., & Chi, E. (2022). Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. ↩

13. Mishra, S., Khashabi, D., Baral, C., Choi, Y., & Hajishirzi, H. (2022). Reframing Instructional Prompts to GPTk’s Language. Findings of the Association for Computational Linguistics: ACL 2022. https://doi.org/10.18653/v1/2022.findings-acl.50 ↩

14. Efrat, A., & Levy, O. (2020). The Turking Test: Can Language Models Understand Instructions? ↩

15. Imani, S., Du, L., & Shrivastava, H. (2023). MathPrompter: Mathematical Reasoning using Large Language Models. ↩

16. Ye, X., & Durrett, G. (2022). The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning. ↩

17. Si, C., Gan, Z., Yang, Z., Wang, S., Wang, J., Boyd-Graber, J., & Wang, L. (2022). Prompting GPT-3 To Be Reliable. ↩

18. Li, Y., Lin, Z., Zhang, S., Fu, Q., Chen, B., Lou, J.-G., & Chen, W. (2022). On the Advance of Making Language Models Better Reasoners. ↩

19. Zhao, T. Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021). Calibrate Before Use: Improving Few-Shot Performance of Language Models. ↩

20. Mitchell, E., Noh, J. J., Li, S., Armstrong, W. S., Agarwal, A., Liu, P., Finn, C., & Manning, C. D. (2022). Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference. ↩

21. Shaikh, O., Zhang, H., Held, W., Bernstein, M., & Yang, D. (2022). On Second Thought, Let’s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning. ↩

22. Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., Chen, C., Olsson, C., Olah, C., Hernandez, D., Drain, D., Ganguli, D., Li, D., Tran-Johnson, E., Perez, E., … Kaplan, J. (2022). Constitutional AI: Harmlessness from AI Feedback. ↩

23. Lake, B. M., & Baroni, M. (2018). Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks. https://doi.org/10.48550/arXiv.1711.00350 ↩

24. Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., & Singh, S. (2020). AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.18653/v1/2020.emnlp-main.346 ↩

25. Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., & Ba, J. (2022). Large Language Models Are Human-Level Prompt Engineers. ↩

26. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language Models are Few-Shot Learners. ↩

27. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. ↩

28. Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., … Fiedel, N. (2022). PaLM: Scaling Language Modeling with Pathways. ↩

29. Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., Yvon, F., Gallé, M., Tow, J., Rush, A. M., Biderman, S., Webson, A., Ammanamanchi, P. S., Wang, T., Sagot, B., Muennighoff, N., del Moral, A. V., … Wolf, T. (2022). BLOOM: A 176B-Parameter Open-Access Multilingual Language Model. ↩

30. Yong, Z.-X., Schoelkopf, H., Muennighoff, N., Aji, A. F., Adelani, D. I., Almubarak, K., Bari, M. S., Sutawika, L., Kasai, J., Baruwa, A., Winata, G. I., Biderman, S., Radev, D., & Nikoulina, V. (2022). BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting. ↩

31. OpenAI. (2023). GPT-4 Technical Report. ↩

32. Lieber, O., Sharir, O., Lentz, B., & Shoham, Y. (2021). Jurassic-1: Technical Details and Evaluation, White paper, AI21 Labs, 2021. URL: Https://Uploads-Ssl. Webflow. Com/60fd4503684b466578c0d307/61138924626a6981ee09caf6_jurassic_ Tech_paper. Pdf. ↩

33. Wang, B., & Komatsuzaki, A. (2021). GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/mesh-transformer-jax. https://github.com/kingoflolz/mesh-transformer-jax ↩

34. 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 Preprint arXiv:1907.11692. ↩

35. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2021). High-Resolution Image Synthesis with Latent Diffusion Models. ↩

36. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical Text-Conditional Image Generation with CLIP Latents. ↩

37. Lester, B., Al-Rfou, R., & Constant, N. (2021). The Power of Scale for Parameter-Efficient Prompt Tuning. ↩

38. Khashabi, D., Lyu, S., Min, S., Qin, L., Richardson, K., Welleck, S., Hajishirzi, H., Khot, T., Sabharwal, A., Singh, S., & Choi, Y. (2021). Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts. ↩

39. Roy, S., & Roth, D. (2015). Solving General Arithmetic Word Problems. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 1743–1752. https://doi.org/10.18653/v1/D15-1202 ↩

40. Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., & Schulman, J. (2021). Training Verifiers to Solve Math Word Problems. ↩

41. Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W. W., Salakhutdinov, R., & Manning, C. D. (2018). HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. ↩

42. Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (2018). FEVER: a large-scale dataset for Fact Extraction and VERification. ↩

43. Parrish, A., Chen, A., Nangia, N., Padmakumar, V., Phang, J., Thompson, J., Htut, P. M., & Bowman, S. R. (2021). BBQ: A Hand-Built Bias Benchmark for Question Answering. ↩

44. Oppenlaender, J. (2022). A Taxonomy of Prompt Modifiers for Text-To-Image Generation. ↩

45. Wang, Z. J., Montoya, E., Munechika, D., Yang, H., Hoover, B., & Chau, D. H. (2022). DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models. ↩

46. Parsons, G. (2022). The DALLE 2 Prompt Book. https://dallery.gallery/the-dalle-2-prompt-book/ ↩

47. Oppenlaender, J. (2022). Prompt Engineering for Text-Based Generative Art. ↩

48. Blake. (2022). With the right prompt, Stable Diffusion 2.0 can do hands. https://www.reddit.com/r/StableDiffusion/comments/z7salo/with_the_right_prompt_stable_diffusion_20_can_do/ ↩

49. Hao, Y., Chi, Z., Dong, L., & Wei, F. (2022). Optimizing Prompts for Text-to-Image Generation. ↩

50. Strobelt, H., Webson, A., Sanh, V., Hoover, B., Beyer, J., Pfister, H., & Rush, A. M. (2022). Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models. arXiv. https://doi.org/10.48550/ARXIV.2208.07852 ↩

51. Bach, S. H., Sanh, V., Yong, Z.-X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., Alyafeai, Z., Dey, M., Santilli, A., Sun, Z., Ben-David, S., Xu, C., Chhablani, G., Wang, H., Fries, J. A., … Rush, A. M. (2022). PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts. ↩

52. Wu, T., Jiang, E., Donsbach, A., Gray, J., Molina, A., Terry, M., & Cai, C. J. (2022). PromptChainer: Chaining Large Language Model Prompts through Visual Programming. ↩

53. Jiang, E., Olson, K., Toh, E., Molina, A., Donsbach, A., Terry, M., & Cai, C. J. (2022). PromptMaker: Prompt-Based Prototyping with Large Language Models. Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491101.3503564 ↩

54. Chase, H. (2022). LangChain (0.0.66) [Computer software]. https://github.com/hwchase17/langchain ↩

55. Tang, T., Junyi, L., Chen, Z., Hu, Y., Yu, Z., Dai, W., Dong, Z., Cheng, X., Wang, Y., Zhao, W., Nie, J., & Wen, J.-R. (2022). TextBox 2.0: A Text Generation Library with Pre-trained Language Models. ↩

56. Ding, N., Hu, S., Zhao, W., Chen, Y., Liu, Z., Zheng, H.-T., & Sun, M. (2021). OpenPrompt: An Open-source Framework for Prompt-learning. arXiv Preprint arXiv:2111.01998. ↩

57. Liu, J. (2022). GPT Index. https://doi.org/10.5281/zenodo.1234 ↩

58. Dohan, D., Xu, W., Lewkowycz, A., Austin, J., Bieber, D., Lopes, R. G., Wu, Y., Michalewski, H., Saurous, R. A., Sohl-dickstein, J., Murphy, K., & Sutton, C. (2022). Language Model Cascades. ↩

59. Karpas, E., Abend, O., Belinkov, Y., Lenz, B., Lieber, O., Ratner, N., Shoham, Y., Bata, H., Levine, Y., Leyton-Brown, K., Muhlgay, D., Rozen, N., Schwartz, E., Shachaf, G., Shalev-Shwartz, S., Shashua, A., & Tenenholtz, M. (2022). ↩

60. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). ↩

61. Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., & Neubig, G. (2022). ↩

62. Liu, V., & Chilton, L. B. (2022). Design Guidelines for Prompt Engineering Text-to-Image Generative Models. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491102.3501825 ↩

63. Crothers, E., Japkowicz, N., & Viktor, H. (2022). Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods. ↩

64. Branch, H. J., Cefalu, J. R., McHugh, J., Hujer, L., Bahl, A., del Castillo Iglesias, D., Heichman, R., & Darwishi, R. (2022). Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples. ↩

65. Kang, D., Li, X., Stoica, I., Guestrin, C., Zaharia, M., & Hashimoto, T. (2023). Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks. ↩

66. Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023). More than you’ve asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models. ↩

67. Willison, S. (2022). Prompt injection attacks against GPT-3. https://simonwillison.net/2022/Sep/12/prompt-injection/ ↩

68. Goodside, R. (2022). Exploiting GPT-3 prompts with malicious inputs that order the model to ignore its previous directions. https://twitter.com/goodside/status/1569128808308957185 ↩

69. Chase, H. (2022). adversarial-prompts. https://github.com/hwchase17/adversarial-prompts ↩

70. KIHO, L. (2023). ChatGPT “DAN” (and other “Jailbreaks”). https://github.com/0xk1h0/ChatGPT_DAN ↩

71. Goodside, R. (2022). GPT-3 Prompt Injection Defenses. https://twitter.com/goodside/status/1578278974526222336?s=20&t=3UMZB7ntYhwAk3QLpKMAbw ↩

72. Mark, C. (2022). Talking to machines: prompt engineering & injection. https://artifact-research.com/artificial-intelligence/talking-to-machines-prompt-engineering-injection/ ↩

73. Selvi, J. (2022). Exploring Prompt Injection Attacks. https://research.nccgroup.com/2022/12/05/exploring-prompt-injection-attacks/ ↩

74. Stuart Armstrong, R. G. (2022). Using GPT-Eliezer against ChatGPT Jailbreaking. https://www.alignmentforum.org/posts/pNcFYZnPdXyL2RfgA/using-gpt-eliezer-against-chatgpt-jailbreaking ↩

75. Liu, K. (2023). The entire prompt of Microsoft Bing Chat?! (Hi, Sydney.). https://twitter.com/kliu128/status/1623472922374574080 ↩

76. Perez, F., & Ribeiro, I. (2022). Ignore Previous Prompt: Attack Techniques For Language Models. arXiv. https://doi.org/10.48550/ARXIV.2211.09527 ↩

77. Brundage, M. (2022). Lessons learned on Language Model Safety and misuse. In OpenAI. OpenAI. https://openai.com/blog/language-model-safety-and-misuse/ ↩

78. Wang, Y.-S., & Chang, Y. (2022). Toxicity Detection with Generative Prompt-based Inference. arXiv. https://doi.org/10.48550/ARXIV.2205.12390 ↩

79. Markov, T. (2022). New and improved content moderation tooling. In OpenAI. OpenAI. https://openai.com/blog/new-and-improved-content-moderation-tooling/ ↩

80. OpenAI. (2022). https://beta.openai.com/docs/guides/moderation ↩

81. OpenAI. (2022). https://openai.com/blog/chatgpt/ ↩

82. Maz, A. (2022). ok I saw a few people jailbreaking safeguards openai put on chatgpt so I had to give it a shot myself. https://twitter.com/alicemazzy/status/1598288519301976064 ↩

83. Piedrafita, M. (2022). Bypass @OpenAI’s ChatGPT alignment efforts with this one weird trick. https://twitter.com/m1guelpf/status/1598203861294252033 ↩

84. Parfait, D. (2022). ChatGPT jailbreaking itself. https://twitter.com/haus_cole/status/1598541468058390534 ↩

85. Soares, N. (2022). Using “pretend” on #ChatGPT can do some wild stuff. You can kind of get some insight on the future, alternative universe. https://twitter.com/NeroSoares/status/1608527467265904643 ↩

86. Moran, N. (2022). I kinda like this one even more! https://twitter.com/NickEMoran/status/1598101579626057728 ↩

87. Degrave, J. (2022). Building A Virtual Machine inside ChatGPT. Engraved. https://www.engraved.blog/building-a-virtual-machine-inside/ ↩

88. Sudo. (2022). https://www.sudo.ws/ ↩

89. Perez, F., & Ribeiro, I. (2022). Ignore Previous Prompt: Attack Techniques For Language Models. arXiv. https://doi.org/10.48550/ARXIV.2211.09527 ↩

90. Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2022). Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Computing Surveys. https://doi.org/10.1145/3560815 ↩

91. Ding, N., & Hu, S. (2022). PromptPapers. https://github.com/thunlp/PromptPapers ↩

92. Perez, E., Ringer, S., Lukošiūtė, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., Jones, A., Chen, A., Mann, B., Israel, B., Seethor, B., McKinnon, C., Olah, C., Yan, D., Amodei, D., … Kaplan, J. (2022). Discovering Language Model Behaviors with Model-Written Evaluations. ↩

93. Su, H., Kasai, J., Wu, C. H., Shi, W., Wang, T., Xin, J., Zhang, R., Ostendorf, M., Zettlemoyer, L., Smith, N. A., & Yu, T. (2022). Selective Annotation Makes Language Models Better Few-Shot Learners. ↩

94. Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., & Grave, E. (2022). Atlas: Few-shot Learning with Retrieval Augmented Language Models. ↩

95. Wang, B., Feng, C., Nair, A., Mao, M., Desai, J., Celikyilmaz, A., Li, H., Mehdad, Y., & Radev, D. (2022). STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension. ↩

96. Significant-Gravitas. (2023). https://news.agpt.co/ ↩

97. Nakajima, Y. (2023). https://github.com/yoheinakajima/babyagi ↩

98. Reworkd.ai. (2023). https://github.com/reworkd/AgentGPT ↩

99. Beurer-Kellner, L., Fischer, M., & Vechev, M. (2022). Prompting Is Programming: A Query Language For Large Language Models. ↩

100. Ratner, N., Levine, Y., Belinkov, Y., Ram, O., Abend, O., Karpas, E., Shashua, A., Leyton-Brown, K., & Shoham, Y. (2022). Parallel Context Windows Improve In-Context Learning of Large Language Models. ↩

101. White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. ↩

102. Bursztyn, V. S., Demeter, D., Downey, D., & Birnbaum, L. (2022). Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models. ↩

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104. Gao, T., Fisch, A., & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). https://doi.org/10.18653/v1/2021.acl-long.295 ↩

105. Liévin, V., Hother, C. E., & Winther, O. (2022). Can large language models reason about medical questions? ↩

106. Dang, H., Mecke, L., Lehmann, F., Goller, S., & Buschek, D. (2022). How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models. ↩

107. Akyürek, A. F., Paik, S., Kocyigit, M. Y., Akbiyik, S., Runyun, Ş. L., & Wijaya, D. (2022). On Measuring Social Biases in Prompt-Based Multi-Task Learning. ↩

108. Jin, Y., Kadam, V., & Wanvarie, D. (2022). Plot Writing From Pre-Trained Language Models. ↩

109. Nadeem, M., Bethke, A., & Reddy, S. (2021). StereoSet: Measuring stereotypical bias in pretrained language models. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 5356–5371. https://doi.org/10.18653/v1/2021.acl-long.416 ↩

110. Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y., Madotto, A., & Fung, P. (2022). Survey of Hallucination in Natural Language Generation. ACM Computing Surveys. https://doi.org/10.1145/3571730 ↩

111. Yuan, A., Coenen, A., Reif, E., & Ippolito, D. (2022). Wordcraft: Story Writing With Large Language Models. 27th International Conference on Intelligent User Interfaces, 841–852. ↩

112. Fadnavis, S., Dhurandhar, A., Norel, R., Reinen, J. M., Agurto, C., Secchettin, E., Schweiger, V., Perini, G., & Cecchi, G. (2022). PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization. arXiv Preprint arXiv:2209.09814. ↩

113. Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., & Hajishirzi, H. (2022). Self-Instruct: Aligning Language Model with Self Generated Instructions. ↩

114. Guo, J., Li, J., Li, D., Tiong, A. M. H., Li, B., Tao, D., & Hoi, S. C. H. (2022). From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models. ↩

115. Schick, T., & Schütze, H. (2020). Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference. ↩

116. Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., & Goldstein, T. (2023). A Watermark for Large Language Models. https://arxiv.org/abs/2301.10226 ↩