PROMPT ENGINEERING: REVOLUTIONIZING NATURAL LANGUAGE PROCESSING

Authors

  • Karthika Gopalakrishnan Data Scientist, USA. Author

Keywords:

NLP, Prompt Engineering, LLM, Banking, Gen AI, Natural Language Processing

Abstract

Natural Language Processing (NLP) has undergone a revolution with the emergence of large language models (LLMs) like GPT and BERT. Prompt engineering, the art of crafting effective inputs to guide LLMs, has become a cornerstone of modern NLP applications. This paper explores how prompt engineering is streamlining NLP processes, revolutionizing industries such as banking, and addressing the inherent risks associated with generative AI. Through practical examples and a discussion of potential pitfalls, we offer strategies for mitigating challenges and maximizing the benefits of prompt engineering in NLP.

References

Brown, T. B., et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33, 1877–1901.

Bommasani, R., et al. (2021). On the Opportunities and Risks of Foundation Models. arXiv preprint arXiv:2108.07258.

Devlin, J., et al. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.

Liu, P., et al. (2021). Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. arXiv preprint arXiv:2107.13586.

Vaswani, A., et al. (2017). Attention is All You Need. Advances in Neural Information Processing Systems, 30, 5998-6008.

Wei, J., et al. (2022). Chain of Thought Prompting Elicits Reasoning in Large Language Models. arXiv preprint arXiv:2201.1190

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Published

2024-10-15

How to Cite

PROMPT ENGINEERING: REVOLUTIONIZING NATURAL LANGUAGE PROCESSING. (2024). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE & MACHINE LEARNING (IJAIML), 3(02), 195-203. https://iaeme-library.com/index.php/IJAIML/article/view/IJAIML_03_02_015