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AI and Prompt Architecture – A Literature Review

by Cassandra Ansara
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 34
Year of Publication: 2023
Authors: Cassandra Ansara
10.5120/ijca2023923133

Cassandra Ansara . AI and Prompt Architecture – A Literature Review. International Journal of Computer Applications. 185, 34 ( Sep 2023), 39-45. DOI=10.5120/ijca2023923133

@article{ 10.5120/ijca2023923133,
author = { Cassandra Ansara },
title = { AI and Prompt Architecture – A Literature Review },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2023 },
volume = { 185 },
number = { 34 },
month = { Sep },
year = { 2023 },
issn = { 0975-8887 },
pages = { 39-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number34/32913-2023923133/ },
doi = { 10.5120/ijca2023923133 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:27:49.259256+05:30
%A Cassandra Ansara
%T AI and Prompt Architecture – A Literature Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 34
%P 39-45
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Prompt Architecture represents a novel and systematic approach to the design and optimization of prompts within Conversational AI systems. This literature review synthesizes key developments, methodologies, and insights in the field, drawing from historical influences, recent advances, and current challenges. The review begins with an examination of early influences, such as Weizenbaum's ELIZA chatbot and Minsky's Frames Paradigm, and proceeds to explore modular prompting strategies, optimization techniques, and evaluation methods. Attention is given to innovative approaches, applications in conversational systems, user-centered design, knowledge representation, and ethical considerations. The review identifies existing gaps in the field, including the need for standardized benchmarks, inclusiveness, and ethical oversight. It concludes with a set of recommended actions for further research and development. The insights and recommendations provided in this review contribute to the maturation of Prompt Architecture as a robust and ethical methodology, with potential implications for the broader field of language model interaction and design.

References
  1. Chen, M. F., Fu, D. Y., Sala, F., Wu, S., Mullapudi, R. T., Poms, F., Fatahalian, K., & Ré, C. 2020. Train and You’ll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings. ArXiv.org. https://doi.org/10.48550/arXiv.2006.15168
  2. Grice, H. P. 1975. Logic and conversation. Academic Press.
  3. Kannan, A., Kurach, K., Ravi, S., Kaufmann, T., Tomkins, A., Miklos, B., Corrado, G., Lukacs, L., Ganea, M., Young, P., & Ramavajjala, V. 2016. Smart Reply: Automated Response Suggestion for Email. ArXiv:1606.04870 [Cs]. https://arxiv.org/abs/1606.04870
  4. Khot, T., Trivedi, H., Finlayson, M., Fu, Y., Richardson, K., Clark, P., & Sabharwal, A. 2023. Decomposed Prompting: A Modular Approach for Solving Complex Tasks. ArXiv.org. https://doi.org/10.48550/arXiv.2210.02406
  5. Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. 2022. Large Language Models are Zero-Shot Reasoners. ArXiv:2205.11916 [Cs]. https://arxiv.org/abs/2205.11916
  6. Lester, B., Al-Rfou, R., & Constant, N. 2021. The Power of Scale for Parameter-Efficient Prompt Tuning. ArXiv:2104.08691 [Cs]. https://arxiv.org/abs/2104.08691
  7. Li, R., Patel, T., & Du, X. 2023. PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations. https://arxiv.org/pdf/2307.02762.pdf
  8. Lieberman, H. 2009. User Interface Goals, AI Opportunities. AI Magazine, 30(4), 16. https://doi.org/10.1609/aimag.v30i4.2266
  9. Lin, B. Y., Zhou, W., Shen, M., Zhou, P., Bhagavatula, C., Choi, Y., & Ren, X. 2020. CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning. ArXiv:1911.03705 [Cs]. https://arxiv.org/abs/1911.03705
  10. Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., & Tang, J. 2021. GPT Understands, Too. ArXiv:2103.10385 [Cs]. https://arxiv.org/abs/2103.10385
  11. Minsky, M. 1981. A Framework for Representing Knowledge. MIT-AI Laboratory Memo 306, June, 1974. https://courses.media.mit.edu/2004spring/mas966/Minsky%201974%20Framework%20for%20knowledge.pdf
  12. Musker, S., & Pavlick, E. 2023. Testing Causal Models of Word Meaning in GPT-3 and -4. ArXiv.org. https://doi.org/10.48550/arXiv.2305.14630
  13. Openai, A., Openai, K., Openai, T., & Openai, I. 2018. Improving Language Understanding by Generative Pre-Training. https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
  14. 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. https://jmlr.org/papers/v21/20-074.html
  15. Rahma Chaabouni, Kharitonov, E., Bouchacourt, D., Dupoux, E., & Baroni, M. 2020. Compositionality and Generalization In Emergent Languages. https://doi.org/10.18653/v1/2020.acl-main.407
  16. Schank, R. C., & Abelson, R. P. 1977. Scripts, Plans, Goals, and Understanding. Lawrence Erlbaum Associates.
  17. Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., & Singh, S. 2020. AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. ArXiv.org. https://doi.org/10.48550/arXiv.2010.15980
  18. Si, C., Gan, Z., Yang, Z., Wang, S., Wang, J., Boyd-Graber, J., & Wang, L. 2022. Prompting GPT-3 To Be Reliable. ArXiv:2210.09150 [Cs]. https://arxiv.org/abs/2210.09150
  19. Sordoni, A., Yuan, X., Côté, M.-A., Pereira, M., Trischler, A., Xiao, Z., Hosseini, A., Niedtner, F., & Roux, N. L. 2023. Deep Language Networks: Joint Prompt Training of Stacked LLMs using Variational Inference. ArXiv.org. https://doi.org/10.48550/arXiv.2306.12509
  20. Wang, Y., & Zhao, Y. 2023. Metacognitive Prompting Improves Understanding in Large Language Models. ArXiv.org. https://doi.org/10.48550/arXiv.2308.05342
  21. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi Quoc, E., Le, V., & Zhou, D. 2023. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models Chain-of-Thought Prompting.
  22. Weizenbaum, J. 1966. ELIZA---a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45. https://doi.org/10.1145/365153.365168
  23. 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. ArXiv:2302.11382 [Cs]. https://arxiv.org/abs/2302.11382
  24. Winograd, T. 1971. Procedures as a Representation for Data in a Computer Program for Understanding Natural Language. Dspace.mit.edu. https://dspace.mit.edu/handle/1721.1/7095
  25. Yang, X., Cheng, W., Zhao, X., Yu, W., Petzold, L., & Chen, H. 2023. Dynamic Prompting: A Unified Framework for Prompt Tuning. ArXiv.org. https://doi.org/10.48550/arXiv.2303.02909
  26. Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. 2023. Tree of Thoughts: Deliberate Problem Solving with Large Language Models. ArXiv.org. https://doi.org/10.48550/arXiv.2305.10601
  27. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. 2023. ReAct: Synergizing Reasoning and Acting in Language Models. ArXiv.org. https://doi.org/10.48550/arXiv.2210.03629.
  28. Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., & Ba, J. 2022. Large Language Models Are Human-Level Prompt Engineers. ArXiv:2211.01910 [Cs]. https://arxiv.org/abs/2211.01910
Index Terms

Computer Science
Information Sciences

Keywords

Prompt Architecture Modular Prompting Chain-of-Thought Prompting Prompt Optimization User-Centered Design Large Language Models (LLMs) ELIZA Chatbot Minsky's Frames Paradigm Unified Text-to-Text Approach Accessibility in AI Ethical Implications of LLMs Evaluation of Prompt Quality Weak Supervision in AI Automated Prompt Generation Conversational AI Applications.

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