This year has seen a revolutionary breakthrough in the field of Large Language Models (LLMs) – their unprecedented capabilities in processing multimodal information have created a wave of positive news and stirred excitement in many sectors. With the potential to solve a plethora of problems, it is essential to ensure that LLMs are provided with the right prompts so that their full capabilities can be tapped into. This has given rise to a new field of research: Prompt Engineering, which involves crafting optimized and task-specific instructions in order to get better responses.
With this in mind, a team of researchers from Mohamed bin Zayed University of AI (MBZUAI) have set out to explore ways of improving the quality of prompts for LLMs. Through their studies, they have formulated 26 guiding principles to ensure optimized prompting. These principles include keeping the prompts clear and concise, making sure they are contextually relevant, aligning them with the specific task, structuring prompts for sequential tasks, and using programming-like logic.
To further evaluate their principles, the researchers have used a manually crafted benchmark called ATLAS. This benchmark consists of 20 questions (with and without the principled prompts) that were tested using LLMs like LLaMA-1, LLaMA-2, GPT-3.5, and GPT-4. The results have been nothing short of remarkable, with an average improvement of 50% across the different models. Furthermore, as the size of the model increases, the accuracy of the prompt engineering principles also increases.
This research paper from MBZUAI has created a comprehensive guide for better prompting of LLMs. While their work may not be able to tackle complex questions yet, it still shows promising results and has the potential to make a significant impact on prompt engineering. So, if you are looking for an effective way to get the most out of your LLMs, don’t forget to check out the paper. And don’t forget to join our 35k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, LinkedIn Group, Twitter, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more!
