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Cited by 405Topic ModelingNatural Language Processing TechniquesSpeech and dialogue systems

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

Prompt Engineering in Large Language Models

Marvin, Ggaliwango, Hellen, Nakayiza, Jjingo, Daudi, Nakatumba-Nabende, Joyce·2024·Algorithms for Intelligent Systems
DOI 10.1007/978-981-99-7962-2_30
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Crossref · 10.1007/978-981-99-7962-2_30

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Citations

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Show all 23 references

Research works printed but not linked: 15

  1. Du Y et al (2023) Guiding pretraining in reinforcement learning with large language models. arXiv cs.LG
  2. Wei J et al (2022) Emergent abilities of large language models. arXiv cs.CL
  3. White J et al (2023) A prompt pattern catalog to enhance prompt engineering with ChatGPT. arXiv cs.SE
  4. Strobelt H et al (2023) Interactive and visual prompt engineering for ad-hoc task adaptation with large language models. IEEE Trans Vis Comput Graph 29(1):1146–1156
  5. Oppenlaender J, Linder R, Silvennoinen J (2023) Prompting AI art: an investigation into the creative skill of prompt engineering. arXiv cs.HC
  6. Trautmann D, Petrova A, Schilder F (2022) Legal prompt engineering for multilingual legal judgement prediction. arXiv cs.CL
  7. Ahmed T, Pai KS, Devanbu P, Barr ET (2023) Improving few-shot prompts with relevant static analysis products. arXiv cs.SE
  8. Diao S, Wang P, Lin Y, Zhang T (2023) Active prompting with chain-of-thought for large language models. arXiv cs.CL
  9. Yang Z et al (2023) MM-REACT: prompting ChatGPT for multimodal reasoning and action. arXiv cs.CV
  10. Maus N, Chao P, Wong E, Gardner J (2023) Adversarial prompting for black box foundation models. arXiv cs.LG
  11. Wang Z, Panda R, Karlinsky L, Feris R, Sun H, Kim Y (2023) Multitask prompt tuning enables parameter-efficient transfer learning. arXiv cs.CL
  12. Kojima T, Gu SS, Reid MM, Matsuo Y, Iwasawa Y (2022) Large language models are zero-shot reasoners. arXiv cs.CL
  13. Zhang T, Wang X, Zhou D, Schuurmans D, Gonzalez JE (2022) TEMPERA: test-time prompting via Reinforcement learning. arXiv cs.CL
  14. Zhou Y et al (2022) Large language models are human-level prompt engineers. arXiv cs.LG
  15. Austin J et al (2021) Program synthesis with large language models. arXiv cs.PL

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Metadata

Authors
Marvin, G., Hellen, N., Jjingo, D., & Nakatumba-Nabende, J.
Affiliations
  • Makerere University (UG)
  • Muni University (UG)
Corresponding author
Marvin, Ggaliwango
ORCID
Venue
Algorithms for Intelligent Systems
Publisher
Springer Nature Singapore
Published
2024
Pages
387-402
ISSN
  • 2524-7565 (ISSN-L)
  • 2524-7573
DOI
10.1007/978-981-99-7962-2_30
OpenAlex
W4390638022
Cited by
403
References
23
Citations by year
2026: 112 · 2025: 227 · 2024: 64
FWCI
119.11
Type
conference paper
Language
en
Open access
closed
Licence
Locations
Algorithms for intelligent systems (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Keywords
prompt engineering, large language models, instruction selection, conversational AI, ChatGPT, process automation, responsible AI, prompt optimization

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