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Papers on “prompt engineering large language models”

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1. A Survey of Large Language Models

   Wayne Xin Zhao, Kun Zhou, Junyi Li, et al. · 2023 · Frontiers Comput. Sci. · 5,010 cites

   The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancements in LLM techniques across four key dimensions: (1) pre-training methodologies, which establish core model capabilities through large-scale self-supervised training, architectural innovations, and data curation strategies; (2) post-training techniques, including supervi

2. BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence

   Wei, Jason, Wang, Xuezhi, Dale Schuurmans, et al. · 2022 · arXiv (Cornell University) · 4,331 cites

   AbstractThere is a failure mode in large language models that we do not have a good name for, and thatwe therefore tend not to treat seriously enough. It is not hallucination — the model is not assertingsomething false. It is not refusal — the model answers at length. It is the production of responses thatcarry the complete outward form of careful reasoning while the cognitive work that reasoning issupposed to represent has not, in any meaningful sense, occurred. We call this theatrical compliance,and we argue that it is, in practical terms, more dangerous than either of the failure modes thatcurrently dominate alignment research. This paper identifies the phenomenon, characterizes its fivep

3. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

   Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al. · 2022 · ACM Computing Surveys · 3,867 cites

   This article surveys and organizes research works in a new paradigm in natural language processing, which we dub “prompt-based learning.” Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P ( y|x ), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original input x is modified using a template into a textual string prompt x′ that has some unfilled slots, and then the language model is used to probabilistically fill the unfilled information to obtain a final string x̂ , from which the final output y can be derived. This framework is powe

4. Learning to Prompt for Vision-Language Models

   Kaiyang Zhou, Jingkang Yang, Chen Change Loy, et al. · 2022 · International Journal of Computer Vision · 3,127 cites

5. A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

   Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, et al. · 2024 · ArXiv · 1,036 cites

   Prompt engineering has emerged as an indispensable technique for extending the capabilities of large language models (LLMs) and vision-language models (VLMs). This approach leverages task-specific instructions, known as prompts, to enhance model efficacy without modifying the core model parameters. Rather than updating the model parameters, prompts allow seamless integration of pre-trained models into downstream tasks by eliciting desired model behaviors solely based on the given prompt. Prompts can be natural language instructions that provide context to guide the model or learned vector representations that activate relevant knowledge. This burgeoning field has enabled success across vario

6. Prompt Engineering with ChatGPT: A Guide for Academic Writers

   Louie Giray · 2023 · Annals of Biomedical Engineering · 771 cites

7. Improving large language models for clinical named entity recognition via prompt engineering

   Yan Hu, Iqra Ameer, X. Zuo, et al. · 2024 · Journal of the American Medical Informatics Association : JAMIA · 389 cites

   Abstract Importance The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models’ performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets. Objectives This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance. Materials and Methods We evaluated these models on 2 clinical NER tasks: (1)

8. Unleashing the potential of prompt engineering for large language models

   B.‐C. CHEN, Zhaofeng Zhang, Nicolas Langrené, et al. · 2025 · Patterns · 338 cites

   This review explores the role of prompt engineering in unleashing the capabilities of large language models (LLMs). Prompt engineering is the process of structuring inputs, and it has emerged as a crucial technique for maximizing the utility and accuracy of these models. Both foundational and advanced prompt engineering methodologies-including techniques such as self-consistency, chain of thought, and generated knowledge, which can significantly enhance the performance of models-are explored in this paper. Additionally, the prompt methods for vision language models (VLMs) are examined in detail. Prompt methods are evaluated with subjective and objective metrics, ensuring a robust analysis of