Radiation fruit: How 1950s'gamma gardens' created today's grapefruit But don't worry: Grapefruits can't hurt you. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Around 1900 when this photo was taken, nearly all grapefruits were yellow. The popular Rio red grapefruit wasn't developed until 1984. Breakthroughs, discoveries, and DIY tips sent six days a week.

Large language models (LLMs) are increasingly used across society, including in domains like business, engineering, and medicine. These fields often grapple with decision-making under uncertainty, a critical yet challenging task. In this paper, we show that directly prompting LLMs on these types of decision-making problems yields poor results, especially as the problem complexity increases. To overcome this limitation, we propose DeLLMa (Decision-making Large Language Model assistant), a framework designed to enhance decision-making accuracy in uncertain environments. DeLLMa involves a multi-step scaffolding procedure, drawing upon principles from decision theory and utility theory, to provide an optimal and human-auditable decision-making process. We validate our framework on decision-making environments involving real agriculture and finance data. Our results show that DeLLMa can significantly improve LLM decision-making performance, achieving up to a 40% increase in accuracy over competing methods.

In the field of Machine Learning, there are two1 predominant strategies to train a machine learning algorithm: supervised learning and unsupervised learning. Common to both strategies is the idea of an example (e.g., an email, an image, a video snippet, etc.) and the translation of these examples into features (e.g., the sender, receiver, and subject of an email). Exactly how this translation works is outside the scope of this article and I leave it as an exercise for the reader. From a high-level perspective, in supervised machine learning, the algorithm is provided with a set of examples such that each example has a label (e.g., whether an email is spam) and the algorithm attempts to "figure out"2 how to map these examples to their corresponding labels. Conversely, in unsupervised learning, an algorithm is given only the examples and attempts to find patterns and similarities between the provided examples.