23 papers · ranked by Valyu relevance
Amaryllis Mavragani, Oscar Tamburis, Abbas Zavar, Bertalan Meskó
Prompt engineering is a relatively new field of research that refers to the practice of designing, refining, and implementing prompts or instructions that guide the output of large language models (LLMs) to help in various tasks. With the emergence of LLMs, the most popular one being ChatGPT that has attracted the…
Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, Shengxin Zhu
Title: Summary 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…
Sifiso Vilakati
Background The integration of generative artificial intelligence (AI), particularly large language models (LLMs), into medical statistics offers transformative potential. However, it also introduces risks of erroneous responses, especially in tasks requiring statistical rigor. Objective To evaluate the effectiveness of…
Taiane de Azevedo Cardoso, Balu Bhasuran, Danqing Hu, Aditi Jain + 6 more
'Jamil Zaghir' 'Marco Naguib' 'Mina Bjelogrlic' 'Aurélie Névéol' 'Xavier Tannier' 'Christian Lovis'] Background Prompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in the…
Oluwole Fagbohun, Rachel M. Harrison, Anton Dereventsov
Due to rapid advancements in the development of Large Language Models (LLMs), programming these models with prompts has recently gained significant attention. However, the sheer number of available prompt engineering techniques creates an overwhelming landscape for practitioners looking to utilize these tools. For the…
Thomas F Heston
The author and publisher are not responsible for any specific health needs that may require medical supervision and are not liable for any damages or negative consequences from any treatment, action, application, or preparation to any person reading or following the information in this book. References are provided for…
Goran Mitrov, Boris Stanoev, Vladimir Trajkovik, Biljana Risteska Stojkoska + 4 more
Background The rapid expansion of digital data poses a unique challenge for retrieving relevant and insightful information efficiently. In particular, the increasing volume of scientific publications has made literature reviews time-consuming. The emergence of large language models (LLMs) offers new opportunities to…
Michael Hewing, Vincent Leinhos
Effective Prompts in Large Language Models Authors: ['Michael Hewing' 'Vincent Leinhos'] The rise of large language models (LLMs) has highlighted the importance of prompt engineering as a crucial technique for optimizing model outputs. While experimentation with various prompting methods, such as Few-shot…
Eshaan Agarwal, Vivek Dani, Tanuja Ganu, Akshay Nambi
Large language models (LLMs) have revolutionized AI across diverse domains, showcasing remarkable capabilities. Central to their success is the concept of prompting, which guides model output generation. However, manual prompt engineering is labor-intensive and domain-specific, necessitating automated solutions. This…
Snehasish Paul, Rohit Kumar, Laxman Das
The field of prompt engineering is becoming an essential phenomenon in artificial intelligence. It is altering how data scientists interact with large language models (LLMs) for analytics applications. This research paper shares empirical results from different studies on prompt engineering with regards to its…
Zhenpeng Chen, Chong Wang, Weisong Sun, Guang Yang + 3 more
'Jie M. Zhang' 'Yang Liu'] Large Language Models (LLMs) are increasingly integrated into software applications, with prompts serving as the primary 'programming' interface to guide their behavior. As a result, a new software paradigm, promptware, has emerged, using natural language prompts to interact with LLMs and…
Saber Soleymani, Nathan Gravel, Krzysztof Kochut, Natarajan Kannan
The integration of large language models (LLMs) with knowledge graphs (KGs) holds significant potential for simplifying the process of querying graph databases, especially for non-technical users. KGs provide a structured representation of domain-specific data, enabling rich and precise information retrieval. However…
Pouyan Esmaeilzadeh
Background The rapid integration of large language models (LLMs) into healthcare raises critical ethical concerns regarding patient safety, reliability, transparency, and equitable care delivery. Despite not being trained explicitly on medical data, individuals increasingly use general-purpose LLMs to address medical…
Rithesh Murthy, Ming Zhu, Liangwei Yang, Jielin Qiu + 5 more
Large Language Models (LLMs) perform best with well-crafted prompts, yet prompt engineering remains manual, inconsistent, and inaccessible to non-experts. We introduce Promptomatix, an automatic prompt optimization framework that transforms natural language task descriptions into high-quality prompts without requiring…
Authors not listed
The Socratic method, grounded in iterative questioning and critical dialogue, offers a compelling framework for leveraging large language models (LLMs) to advance scientific reasoning and discovery in chemistry and materials science. In this paper, we explore how Socratic principles can be integrated into prompt…
Rachel Knapp, Braidon Johnson, Lucas Busta
Premise: Recently, plant science has seen transformative advances in scalable data collection for sequence and chemical data. These large datasets, combined with machine learning, revealed that conducting plant metabolic research on large scales yields remarkable insights. A key next step in increasing scale has been…
Pieter Floris Jacobs, Robert Pollice
Scientists across domains are often challenged to master domain-specific languages (DSLs) for their research, which are merely a means to an end but are pervasive in fields like computational chemistry. Automated code generation promises to overcome this barrier, allowing researchers to focus on their core expertise.…
Yibo Chen, Jeffrey Gao, Marius Petruc, Richard D. Hammer + 2 more
ChatGPT has demonstrated its potential as a surrogate knowledge graph. Trained on extensive data sources, including open-access publications, peer-reviewed research articles and biomedical websites, ChatGPT extracted information on gene relationships and biological pathways. However, a major challenge is model…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…
Katie Fan
Metabolic engineering in plants has emerged as a powerful approach to address global challenges in agriculture, nutrition, and sustainability. This comprehensive review explores cutting-edge strategies for manipulating primary and secondary metabolic pathways in plants, utilizing advanced genetic modification tools to…
Jacob Beal, Brian Teague, John T. Sexton, Sebastian Castillo-Hair + 4 more
Reliable, predictable engineering of cellular behavior is one of the key goals of synthetic biology. As the field matures, biological engineers will become increasingly reliant on computer models that allow for the rapid exploration of design space prior to the more costly construction and characterization of candidate…
Authors not listed
Bioprocess engineering has incorporated effective AI applications in recent years that consist of traditional approaches to training models on relevant data to then analyze and predict new and unseen data. The missing component has been the ability to process mixed data from an assortment of dissimilar information…
Jia Dong, Seth W. Croslow, Stephan T. Lane, Daniel C. Castro + 10 more
Plant bioengineering is a time-consuming and labor-intensive process, with no guarantee of achieving the desired trait. Here we report a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB). FAST-PB achieves gene cloning, genome editing, and product characterization by integrating…