This systematic survey organizes prompt engineering into a taxonomy of 58 LLM techniques and 40 others, supplies a shared vocabulary, and offers guidelines for state-of-the-art models.
Eshaan Tanwar, Subhabrata Dutta, Manish Borthakur, and Tanmoy Chakraborty
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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SERE improves LLM performance on event causality identification by selecting few-shot examples via three structural metrics to mitigate causal bias.
Generative AI systems arise from statistical data processing that produces human-like outputs, creating a mismatch with traditional computer expectations and positioning educational researchers to lead in studying and applying them.
citing papers explorer
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The Prompt Report: A Systematic Survey of Prompt Engineering Techniques
This systematic survey organizes prompt engineering into a taxonomy of 58 LLM techniques and 40 others, supplies a shared vocabulary, and offers guidelines for state-of-the-art models.
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SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification
SERE improves LLM performance on event causality identification by selecting few-shot examples via three structural metrics to mitigate causal bias.
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Generative AI Technologies, Techniques & Tensions: A Primer
Generative AI systems arise from statistical data processing that produces human-like outputs, creating a mismatch with traditional computer expectations and positioning educational researchers to lead in studying and applying them.